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Jang M, Gupta A, Kovanlikaya A, Scholl JE, Zun Z. High-resolution anatomical imaging of the fetal brain with a reduced field of view using outer volume suppression. Magn Reson Med 2024; 92:1556-1567. [PMID: 38702999 PMCID: PMC11262973 DOI: 10.1002/mrm.30147] [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: 01/31/2024] [Revised: 04/04/2024] [Accepted: 04/19/2024] [Indexed: 05/06/2024]
Abstract
PURPOSE To achieve high-resolution fetal brain anatomical imaging without introducing image artifacts by reducing the FOV, and to demonstrate improved image quality compared to conventional full-FOV fetal brain imaging. METHODS Reduced FOV was achieved by applying outer volume suppression (OVS) pulses immediately prior to standard single-shot fast spin echo (SSFSE) imaging. In the OVS preparation, a saturation RF pulse followed by a gradient spoiler was repeated three times with optimized flip-angle weightings and a variable spoiler scheme to enhance signal suppression. Simulations and phantom and in-vivo experiments were performed to evaluate OVS performance. In-vivo high-resolution SSFSE images acquired using the proposed approach were compared with conventional and high-resolution SSFSE images with a full FOV, using image quality scores assessed by neuroradiologists and calculated image metrics. RESULTS Excellent signal suppression in the saturation bands was confirmed in phantom and in-vivo experiments. High-resolution SSFSE images with a reduced FOV acquired using OVS demonstrated the improved depiction of brain structures without significant motion and blurring artifacts. The proposed method showed the highest image quality scores in the criteria of sharpness, contrast, and artifact and was selected as the best method based on overall image quality. The calculated image sharpness and tissue contrast ratio were also the highest with the proposed method. CONCLUSION High-resolution fetal brain anatomical images acquired using a reduced FOV with OVS demonstrated improved image quality both qualitatively and quantitatively, suggesting the potential for enhanced diagnostic accuracy in detecting fetal brain abnormalities in utero.
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Affiliation(s)
- MinJung Jang
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Ajay Gupta
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Arzu Kovanlikaya
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Jessica E. Scholl
- Department of Obstetrics and Gynecology, Weill Cornell Medicine, New York, New York, USA
| | - Zungho Zun
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
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2
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Zimmermann M, Abbas Z, Sommer Y, Lewin A, Ramkiran S, Felder J, Worthoff WA, Oros-Peusquens AM, Yun SD, Shah NJ. QRAGE-Simultaneous multiparametric quantitative MRI of water content, T 1, T 2*, and magnetic susceptibility at ultrahigh field strength. Magn Reson Med 2024. [PMID: 39219160 DOI: 10.1002/mrm.30272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 07/26/2024] [Accepted: 08/10/2024] [Indexed: 09/04/2024]
Abstract
PURPOSE To introduce quantitative rapid gradient-echo (QRAGE), a novel approach for the simultaneous mapping of multiple quantitative MRI parameters, including water content, T1, T2*, and magnetic susceptibility at ultrahigh field strength. METHODS QRAGE leverages a newly developed multi-echo MPnRAGE sequence, facilitating the acquisition of 171 distinct contrast images across a range of inversion and TE points. To maintain a short acquisition time, we introduce MIRAGE2, a novel model-based reconstruction method that exploits prior knowledge of temporal signal evolution, represented as damped complex exponentials. MIRAGE2 minimizes local Block-Hankel and Casorati matrices. Parameter maps are derived from the reconstructed contrast images through postprocessing steps. We validate QRAGE through extensive simulations, phantom studies, and in vivo experiments, demonstrating its capability for high-precision imaging. RESULTS In vivo brain measurements show the promising performance of QRAGE, with test-retest SDs and deviations from reference methods of < 0.8% for water content, < 17 ms for T1, and < 0.7 ms for T2*. QRAGE achieves whole-brain coverage at a 1-mm isotropic resolution in just 7 min and 15 s, comparable to the acquisition time of an MP2RAGE scan. In addition, QRAGE generates a contrast image akin to the UNI image produced by MP2RAGE. CONCLUSION QRAGE is a new, successful approach for simultaneously mapping multiple MR parameters at ultrahigh field.
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Affiliation(s)
- Markus Zimmermann
- Forschungszentrum Jülich, Institute of Neuroscience and Medicine-4, Jülich, Germany
| | - Zaheer Abbas
- Forschungszentrum Jülich, Institute of Neuroscience and Medicine-4, Jülich, Germany
| | - Yannic Sommer
- Forschungszentrum Jülich, Institute of Neuroscience and Medicine-4, Jülich, Germany
| | - Alexander Lewin
- Forschungszentrum Jülich, Institute of Neuroscience and Medicine-11, Jülich, Germany
| | - Shukti Ramkiran
- Forschungszentrum Jülich, Institute of Neuroscience and Medicine-4, Jülich, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
| | - Jörg Felder
- Forschungszentrum Jülich, Institute of Neuroscience and Medicine-4, Jülich, Germany
- RWTH Aachen University, Aachen, Germany
| | - Wieland A Worthoff
- Forschungszentrum Jülich, Institute of Neuroscience and Medicine-4, Jülich, Germany
| | | | - Seong Dae Yun
- Forschungszentrum Jülich, Institute of Neuroscience and Medicine-4, Jülich, Germany
| | - N Jon Shah
- Forschungszentrum Jülich, Institute of Neuroscience and Medicine-4, Jülich, Germany
- Forschungszentrum Jülich, Institute of Neuroscience and Medicine-11, Jülich, Germany
- JARA-BRAIN-Translational Medicine, Aachen, Germany
- Department of Neurology, RWTH Aachen University, Aachen, Germany
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Marhabaie S, Labbé A, Quesson B, Poirier-Quinot M. The Minimum Admissible Detuning Efficiency of MRI Receive-Only Surface Coils. J Magn Reson Imaging 2024; 60:777-788. [PMID: 38305462 DOI: 10.1002/jmri.29209] [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: 06/27/2023] [Revised: 10/26/2023] [Accepted: 10/27/2023] [Indexed: 02/03/2024] Open
Abstract
BACKGROUND The minimum admissible detuning efficiency (DE) of a receive coil is an essential parameter for coil designers. A receive coil with inefficient detuning leads to inhomogeneous B1 during excitation. Previously proposed criteria for quantifying the DE rely on indirect measurements and are difficult to implement. PURPOSE To present an alternative method to quantify the DE of receive-only surface coils. STUDY TYPE Theoretical study supported by simulations and phantom experiments. PHANTOMS Uniform spherical (100 mm diameter) and cylindrical (66 mm diameter) phantoms. FIELD STRENGTH/SEQUENCE Dual repetition time B1 mapping sequence at 1.5T, and Bloch-Siegert shift B1 mapping sequence at 3.0T. ASSESSMENT One non-planar (80 × 43 mm2) and two planar (40 and 57 mm diameter) surface coils were built. Theoretical analysis was performed to determine the minimum DE required to avoid B1 distortions. Experimental B1 maps were acquired for the non-planar and planar surface coils at both 1.5T and 3.0T and visually compared with simulated B1 maps to assess the validity of the theoretical analysis. STATISTICAL TESTS None. RESULTS Based on the theoretical analysis, the proposed minimum admissible DE, defined as DEthr = 20 Log (Q) + 13 dB, depended only on the quality factor (Q) of the coil and was independent of coil area and field strength. Simulations and phantom experiments showed that when the DE was higher than this minimum threshold level, the B1 field generated by the transmission coil was not modified by the receive coil. DATA CONCLUSION The proposed criterion for assessing the DE is simple to measure, and does not depend on the area of the coil or on the magnetic field strength, up to 3T. Experimental and simulated B1 maps confirmed that detuning efficiencies above the theoretically derived minimal admissible DE resulted in a non-distorted B1 field. EVIDENCE LEVEL 2 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Sina Marhabaie
- Université Paris-Saclay, CNRS, Inserm, Laboratoire d'Imagerie Biomédicale Multimodale Paris Saclay, Orsay, France
| | - Aimé Labbé
- Université Paris-Saclay, CNRS, Inserm, Laboratoire d'Imagerie Biomédicale Multimodale Paris Saclay, Orsay, France
| | - Bruno Quesson
- University of Bordeaux, CNRS, CRMSB, UMR 5536, IHU Liryc, Bordeaux, France
| | - Marie Poirier-Quinot
- Université Paris-Saclay, CNRS, Inserm, Laboratoire d'Imagerie Biomédicale Multimodale Paris Saclay, Orsay, France
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Sørland KI, Trimble CG, Wu CY, Bathen TF, Elschot M, Cloos MA. Reducing femoral flow artefacts in radial magnetic resonance fingerprinting of the prostate using region-optimised virtual coils. NMR IN BIOMEDICINE 2024; 37:e5136. [PMID: 38514929 DOI: 10.1002/nbm.5136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 01/19/2024] [Accepted: 02/07/2024] [Indexed: 03/23/2024]
Abstract
High acceleration factors in radial magnetic resonance fingerprinting (MRF) of the prostate lead to strong streak-like artefacts from flow in the femoral blood vessels, possibly concealing important anatomical information. Region-optimised virtual (ROVir) coils is a beamforming-based framework to create virtual coils that maximise signal in a region of interest while minimising signal in a region of interference. In this study, the potential of removing femoral flow streak artefacts in prostate MRF using ROVir coils is demonstrated in silico and in vivo. The ROVir framework was applied to radial MRF k-space data in an automated pipeline designed to maximise prostate signal while minimising signal from the femoral vessels. The method was tested in 15 asymptomatic volunteers at 3 T. The presence of streaks was visually assessed and measurements of whole prostate T1, T2 and signal-to-noise ratio (SNR) with and without streak correction were examined. In addition, a purpose-built simulation framework in which blood flow through the femoral vessels can be turned on and off was used to quantitatively evaluate ROVir's ability to suppress streaks in radial prostate MRF. In vivo it was shown that removing selected ROVir coils visibly reduces streak-like artefacts from the femoral blood flow, without increasing the reconstruction time. On average, 80% of the prostate SNR was retained. A similar reduction of streaks was also observed in silico, while the quantitative accuracy of T1 and T2 mapping was retained. In conclusion, ROVir coils efficiently suppress streaking artefacts from blood flow in radial MRF of the prostate, thereby improving the visual clarity of the images, without significant sacrifices to acquisition time, reconstruction time and accuracy of quantitative values. This is expected to help enable T1 and T2 mapping of prostate cancer in clinically viable times, aiding differentiation between prostate cancer from noncancer and healthy prostate tissue.
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Affiliation(s)
- Kaia I Sørland
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
| | - Christopher G Trimble
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Radiology and Nuclear Medicine, St. Olavs hospital, Trondheim University Hostpital, Trondheim, Norway
| | - Chia-Yin Wu
- Centre for Advanced Imaging, The University of Queensland, St Lucia, Queensland, Australia
- ARC Training Centre for Innovation on Biomedical Imaging Technology (CIBIT), The University of Queensland, St Lucia, Queensland, Australia
- School of Electrical Engineering and Computer Science, The University of Queensland, St Lucia, Queensland, Australia
| | - Tone F Bathen
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Radiology and Nuclear Medicine, St. Olavs hospital, Trondheim University Hostpital, Trondheim, Norway
| | - Mattijs Elschot
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Radiology and Nuclear Medicine, St. Olavs hospital, Trondheim University Hostpital, Trondheim, Norway
| | - Martijn A Cloos
- Centre for Advanced Imaging, The University of Queensland, St Lucia, Queensland, Australia
- ARC Training Centre for Innovation on Biomedical Imaging Technology (CIBIT), The University of Queensland, St Lucia, Queensland, Australia
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Versteeg E, Liu H, van der Heide O, Fuderer M, van den Berg CAT, Sbrizzi A. High SNR full brain relaxometry at 7T by accelerated MR-STAT. Magn Reson Med 2024; 92:226-235. [PMID: 38326909 DOI: 10.1002/mrm.30052] [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: 11/01/2023] [Revised: 12/21/2023] [Accepted: 01/26/2024] [Indexed: 02/09/2024]
Abstract
PURPOSE To demonstrate the feasibility and robustness of the Magnetic Resonance Spin TomogrAphy in Time-domain (MR-STAT) framework for fast, high SNR relaxometry at 7T. METHODS To deploy MR-STAT on 7T-systems, we designed optimized flip-angles using the BLAKJac-framework that incorporates the SAR-constraints. Transmit RF-inhomogeneities were mitigated by including a measuredB 1 + $$ {B}_1^{+} $$ -map in the reconstruction. Experiments were performed on a gel-phantom and on five volunteers to explore the robustness of the sequence and its sensitivity toB 1 + $$ {B}_1^{+} $$ inhomogeneities. The SNR-gain at 7T was explored by comparing phantom and in vivo results to MR-STAT at 3T in terms of SNR-efficiency. RESULTS The higher SNR at 7T enabled two-fold acceleration with respect to current 2D MR-STAT protocols at lower field strengths. The resulting scan had whole-brain coverage, with 1 x 1 x 3 mm3 resolution (1.5 mm slice-gap) and was acquired within 3 min including theB 1 + $$ {B}_1^{+} $$ -mapping. AfterB 1 + $$ {B}_1^{+} $$ -correction, the estimated T1 and T2 in a phantom showed a mean relative error of, respectively, 1.7% and 4.4%. In vivo, the estimated T1 and T2 in gray and white matter corresponded to the range of values reported in literature with a variation over the subjects of 1.0%-2.1% (WM-GM) for T1 and 4.3%-5.3% (WM-GM) for T2. We measured a higher SNR-efficiency at 7T (R = 2) than at 3T for both T1 and T2 with, respectively, a 4.1 and 2.3 times increase in SNR-efficiency. CONCLUSION We presented an accelerated version of MR-STAT tailored to high field (7T) MRI using a low-SAR flip-angle train and showed high quality parameter maps with an increased SNR-efficiency compared to MR-STAT at 3T.
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Affiliation(s)
- Edwin Versteeg
- Computational Imaging Group, Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Hongyan Liu
- Computational Imaging Group, Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Oscar van der Heide
- Computational Imaging Group, Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Miha Fuderer
- Computational Imaging Group, Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Cornelis A T van den Berg
- Computational Imaging Group, Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Alessandro Sbrizzi
- Computational Imaging Group, Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
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Byun H, Han D, Chun HJ, Lee SW. Multiparametric quantification of T1 and T2 relaxation time of bone metastasis in comparison with red or fatty bone marrow using magnetic resonance fingerprinting. Skeletal Radiol 2024; 53:1071-1080. [PMID: 38041749 DOI: 10.1007/s00256-023-04521-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 11/12/2023] [Accepted: 11/17/2023] [Indexed: 12/03/2023]
Abstract
OBJECTIVES To assess the T1 and T2 values of bone marrow lesions in spine and pelvis derived from magnetic resonance fingerprinting (MRF) and to evaluate the differences in values among bone metastasis, red marrow and fatty marrow. METHODS Sixty patients who underwent lumbar spine and pelvic MRI with magnetic resonance fingerprinting were retrospectively included. Among eligible patients, those with bone metastasis, benign red marrow deposition and normal fatty marrow were identified. Two radiologists independently measured the T1 and T2 values from metastatic bone lesions, fatty marrow, and red marrow deposition on three-dimensional-magnetic resonance fingerprinting. Intergroup comparison and interobserver agreement were analyzed. RESULTS T1 relaxation time was significantly higher in osteoblastic metastasis than in red marrow (1674.6 ± 436.3 vs 858.7 ± 319.5, p < .001). Intraclass correlation coefficients for T1 and T2 values were 0.96 (p < 0.001) and 0.83 (p < 0.001), respectively. T2 relaxation time of osteoblastic metastasis and red marrow deposition had no evidence of a difference (osteoblastic metastasis, 57.9 ± 25.0 vs red marrow, 58.0 ± 34.4, p = 0.45), as were the average T2 values of osteolytic metastasis and red marrow deposition (osteolytic metastasis, 45.3 ± 15.1 vs red marrow, 58.0 ± 34.4, p = 0.63). CONCLUSIONS We report the feasibility of three-dimensional-magnetic resonance fingerprinting based quantification of bone marrow to differentiate bone metastasis from red marrow. Simultaneous T1 and T2 quantification of metastasis and red marrow deposition was possible in spine and pelvis and showed significant different values with excellent inter-reader agreement. ADVANCE IN KNOWLEDGE T1 values from three-dimensional-magnetic resonance fingerprinting might be a useful quantifier for evaluating bone marrow lesions.
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Affiliation(s)
- Hokyun Byun
- Department of Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 1021 Tongil Ro, Eunpyeong-Gu, Seoul, Republic of Korea
| | - Dongyeob Han
- Siemens Healthineers Ltd, Seoul, Republic of Korea
| | - Ho Jong Chun
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-Daero, Seocho-Gu, Seoul, Republic of Korea.
| | - Sheen-Woo Lee
- Department of Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 1021 Tongil Ro, Eunpyeong-Gu, Seoul, Republic of Korea.
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Dietrich KA, Klüter S, Dinkel F, Echner G, Brons S, Orzada S, Debus J, Ladd ME, Platt T. An essentially radiation-transparent body coil integrated with a patient rotation system for MR-guided particle therapy. Med Phys 2024; 51:4028-4043. [PMID: 38656549 DOI: 10.1002/mp.17065] [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: 04/11/2023] [Revised: 12/28/2023] [Accepted: 03/20/2024] [Indexed: 04/26/2024] Open
Abstract
BACKGROUND The pursuit of adaptive radiotherapy using MR imaging for better precision in patient positioning puts stringent demands on the hardware components of the MR scanner. Particularly in particle therapy, the dose distribution and thus the efficacy of the treatment is susceptible to beam attenuation from interfering materials in the irradiation path. This severely limits the usefulness of conventional imaging coils, which contain highly attenuating parts such as capacitors and preamplifiers in an unknown position, and requires development of a dedicated radiofrequency (RF) coil with close consideration of the materials and components used. PURPOSE In MR-guided radiation therapy in the human torso, imaging coils with a large FOV and homogeneous B1 field distribution are required for reliable tissue classification. In this work, an imaging coil for MR-guided particle therapy was developed with minimal ion attenuation while maintaining flexibility in treatment. METHODS A birdcage coil consisting of nearly radiation-transparent materials was designed and constructed for a closed-bore 1.5 T MR system. Additionally, the coil was mounted on a rotatable patient capsule for flexible positioning of the patient relative to the beam. The ion attenuation of the RF coil was investigated in theory and via measurements of the Bragg peak position. To characterize the imaging quality of the RF coil, transmit and receive field distributions were simulated and measured inside a homogeneous tissue-simulating phantom for various rotation angles of the patient capsule ranging from 0° to 345° in steps of 15°. Furthermore, simulations with a heterogeneous human voxel model were performed to better estimate the effect of real patient loading, and the RF coil was compared to the internal body coil in terms of SNR for a full rotation of the patient capsule. RESULTS The RF coil (total water equivalent thickness (WET) ≈ 420 µm, WET of conductor ≈ 210 µm) can be considered to be radiation-transparent, and a measured transmit power efficiency (B1 +/P $\sqrt {\mathrm{P}} $ ) between 0.17 µT/W $\sqrt {\mathrm{W}} $ and 0.26 µT/W $\sqrt {\mathrm{W}} $ could be achieved in a volume (Δz = 216 mm, complete x and y range) for the 24 investigated rotation angles of the patient capsule. Furthermore, homogeneous transmit and receive field distributions were measured and simulated in the transverse, coronal and sagittal planes in a homogeneous phantom and a human voxel model. In addition, the SNR of the radiation-transparent RF coil varied between 103 and 150, in the volume (Δz = 216 mm) of a homogeneous phantom and surpasses the SNR of the internal body coil for all rotation angles of the patient capsule. CONCLUSIONS A radiation-transparent RF coil was developed and built that enables flexible patient to beam positioning via full rotation capability of the RF coil and patient relative to the beam, with results providing promising potential for adaptive MR-guided particle therapy.
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Affiliation(s)
- Kilian A Dietrich
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
- Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Physics, Heidelberg University, Heidelberg, Germany
- Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Sebastian Klüter
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
- National Center for Radiation Research in Oncology (NCRO), Heidelberg, Germany
- Heidelberg Institute of Radiation Oncology (HIRO), Heidelberg, Germany
| | - Fabian Dinkel
- Heidelberg Institute of Radiation Oncology (HIRO), Heidelberg, Germany
- Division of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Gernot Echner
- Heidelberg Institute of Radiation Oncology (HIRO), Heidelberg, Germany
- Division of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Stephan Brons
- Heidelberg Ion-Beam Therapy Center (HIT), Heidelberg University Hospital, Heidelberg, Germany
| | - Stephan Orzada
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
- Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jürgen Debus
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
- Faculty of Physics, Heidelberg University, Heidelberg, Germany
- Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- National Center for Radiation Research in Oncology (NCRO), Heidelberg, Germany
- Heidelberg Institute of Radiation Oncology (HIRO), Heidelberg, Germany
- Heidelberg Ion-Beam Therapy Center (HIT), Heidelberg University Hospital, Heidelberg, Germany
- German Cancer Consortium (DKTK), Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
- Faculty of Medicine, Heidelberg University, Heidelberg, Germany
| | - Mark E Ladd
- Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Physics, Heidelberg University, Heidelberg, Germany
- Faculty of Medicine, Heidelberg University, Heidelberg, Germany
| | - Tanja Platt
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
- Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
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Heydari A, Ahmadi A, Kim TH, Bilgic B. Joint MAPLE: Accelerated joint T 1 and T 2 * $$ {{\mathrm{T}}_2}^{\ast } $$ mapping with scan-specific self-supervised networks. Magn Reson Med 2024; 91:2294-2309. [PMID: 38181183 PMCID: PMC11007829 DOI: 10.1002/mrm.29989] [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: 07/19/2023] [Revised: 10/30/2023] [Accepted: 12/11/2023] [Indexed: 01/07/2024]
Abstract
PURPOSE Quantitative MRI finds important applications in clinical and research studies. However, it is encoding intensive and may suffer from prohibitively long scan times. Accelerated MR parameter mapping techniques have been developed to help address these challenges. Here, an accelerated joint T1,T 2 * $$ {{\mathrm{T}}_2}^{\ast } $$ , frequency and proton density mapping technique with scan-specific self-supervised network reconstruction is proposed to synergistically combine parallel imaging, model-based, and deep learning approaches to speed up parameter mapping. METHODS Proposed framework, Joint MAPLE, includes parallel imaging, signal modeling, and data consistency blocks which are optimized jointly in a combined loss function. A scan-specific self-supervised reconstruction is embedded into the framework, which takes advantage of multi-contrast data from a multi-echo, multi-flip angle, gradient echo acquisition. RESULTS In comparison with parallel reconstruction techniques powered by low-rank methods, emerging scan specific networks, and model-basedT 2 * $$ {{\mathrm{T}}_2}^{\ast } $$ estimation approaches, the proposed framework reduces the reconstruction error in parameter maps by approximately two-fold on average at acceleration rates as high as R = 16 with uniform sampling. It can outperform evaluated parallel reconstruction techniques up to four-fold on average in the presence of challenging sub-sampling masks. It is observed that Joint MAPLE performs well at extreme acceleration rates of R = 25 and R = 36 with error values less than 20%. CONCLUSION Joint MAPLE enables higher fidelity parameter estimation at high acceleration rates by synergistically combining parallel imaging and model-based parameter mapping and exploiting multi-echo, multi-flip angle datasets. Utilizing a scan-specific self-supervised reconstruction obviates the need for large data sets for training while improving the parameter estimation ability.
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Affiliation(s)
- Amir Heydari
- Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran
| | - Abbas Ahmadi
- Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran
| | - Tae Hyung Kim
- Department of Computer Engineering, Hongik University, Seoul, Korea
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
- Radiology, Harvard Medical School, Boston, MA, United States
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
- Radiology, Harvard Medical School, Boston, MA, United States
- Harvard/MIT Health Sciences and Technology, Cambridge, MA, United States
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9
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Wang H, Lu Y. Editorial for "Characterization of Age-Related and Sex-Related Differences of Relaxation Parameters in the Intervertebral Disc Using MR-Fingerprinting". J Magn Reson Imaging 2024; 59:1325-1326. [PMID: 37522412 DOI: 10.1002/jmri.28929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 06/22/2023] [Indexed: 08/01/2023] Open
Affiliation(s)
- Hanqi Wang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yong Lu
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Monga A, Singh D, de Moura HL, Zhang X, Zibetti MVW, Regatte RR. Emerging Trends in Magnetic Resonance Fingerprinting for Quantitative Biomedical Imaging Applications: A Review. Bioengineering (Basel) 2024; 11:236. [PMID: 38534511 DOI: 10.3390/bioengineering11030236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 02/21/2024] [Accepted: 02/22/2024] [Indexed: 03/28/2024] Open
Abstract
Magnetic resonance imaging (MRI) stands as a vital medical imaging technique, renowned for its ability to offer high-resolution images of the human body with remarkable soft-tissue contrast. This enables healthcare professionals to gain valuable insights into various aspects of the human body, including morphology, structural integrity, and physiological processes. Quantitative imaging provides compositional measurements of the human body, but, currently, either it takes a long scan time or is limited to low spatial resolutions. Undersampled k-space data acquisitions have significantly helped to reduce MRI scan time, while compressed sensing (CS) and deep learning (DL) reconstructions have mitigated the associated undersampling artifacts. Alternatively, magnetic resonance fingerprinting (MRF) provides an efficient and versatile framework to acquire and quantify multiple tissue properties simultaneously from a single fast MRI scan. The MRF framework involves four key aspects: (1) pulse sequence design; (2) rapid (undersampled) data acquisition; (3) encoding of tissue properties in MR signal evolutions or fingerprints; and (4) simultaneous recovery of multiple quantitative spatial maps. This paper provides an extensive literature review of the MRF framework, addressing the trends associated with these four key aspects. There are specific challenges in MRF for all ranges of magnetic field strengths and all body parts, which can present opportunities for further investigation. We aim to review the best practices in each key aspect of MRF, as well as for different applications, such as cardiac, brain, and musculoskeletal imaging, among others. A comprehensive review of these applications will enable us to assess future trends and their implications for the translation of MRF into these biomedical imaging applications.
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Affiliation(s)
- Anmol Monga
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Dilbag Singh
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Hector L de Moura
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Xiaoxia Zhang
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Marcelo V W Zibetti
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Ravinder R Regatte
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
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11
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Elsaid NMH, Tagare HD, Galiana G. A Physics-Based Algorithm to Universally Standardize Routinely Obtained Clinical T 2-Weighted Images. Acad Radiol 2024; 31:582-595. [PMID: 37407374 PMCID: PMC10761595 DOI: 10.1016/j.acra.2023.05.036] [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: 02/09/2023] [Revised: 05/15/2023] [Accepted: 05/29/2023] [Indexed: 07/07/2023]
Abstract
RATIONALE AND OBJECTIVES MR images can be challenging for machine learning and other large-scale analyses because most clinical images, for example, T2-weighted (T2w) images, reflect not only the biologically relevant T2 of tissue but also hardware and acquisition parameters that vary from site to site. Quantitative T2 mapping avoids these confounds because it quantitatively isolates the biological parameter of interest, thus representing a universal standardization across sites. However, efforts to incorporate quantitative mapping sequences into routine clinical practice have seen slow adoption. Here we show, for the first time, that the routine T2w complex raw dataset can be successfully regarded as a quantitative mapping sequence that can be reconstructed with classical optimization methods and physics-based constraints. MATERIALS AND METHODS While previous constrained reconstruction methods are unable to reconstruct a T2 map based on this data, the expanding-constrained alternating minimization for parameter mapping (e-CAMP), which employs stepwise initialization, a linearized version of the exponential model and a phase conjugacy constraint, is demonstrated to provide useful quantitative maps directly from a vendor T2w single image data. RESULTS This paper introduces the method and demonstrates its performance using simulations, retrospectively undersampled brain images, and prospectively acquired T2w images taken on both phantom and brain. CONCLUSION Because T2w scans are included in nearly every protocol, this approach could open the door to creating large, standardized datasets without requiring widespread changes in clinical protocols.
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Affiliation(s)
- Nahla M H Elsaid
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, 300 Cedar St, New Haven, CT 06519 (N.M.H.E., H.D.T., G.G.).
| | - Hemant D Tagare
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, 300 Cedar St, New Haven, CT 06519 (N.M.H.E., H.D.T., G.G.); Department of Biomedical Engineering, Yale University, New Haven, Connecticut (H.D.T., G.G.)
| | - Gigi Galiana
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, 300 Cedar St, New Haven, CT 06519 (N.M.H.E., H.D.T., G.G.); Department of Biomedical Engineering, Yale University, New Haven, Connecticut (H.D.T., G.G.)
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12
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Kumar S, Saber H, Charron O, Freeman L, Tamir JI. Correcting synthetic MRI contrast-weighted images using deep learning. Magn Reson Imaging 2024; 106:43-54. [PMID: 38092082 DOI: 10.1016/j.mri.2023.11.015] [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: 08/30/2023] [Revised: 11/30/2023] [Accepted: 11/30/2023] [Indexed: 12/17/2023]
Abstract
Synthetic magnetic resonance imaging (MRI) offers a scanning paradigm where a fast multi-contrast sequence can be used to estimate underlying quantitative tissue parameter maps, which are then used to synthesize any desirable clinical contrast by retrospectively changing scan parameters in silico. Two benefits of this approach are the reduced exam time and the ability to generate arbitrary contrasts offline. However, synthetically generated contrasts are known to deviate from the contrast of experimental scans. The reason for contrast mismatch is the necessary exclusion of some unmodeled physical effects such as partial voluming, diffusion, flow, susceptibility, magnetization transfer, and more. The inclusion of these effects in signal encoding would improve the synthetic images, but would make the quantitative imaging protocol impractical due to long scan times. Therefore, in this work, we propose a novel deep learning approach that generates a multiplicative correction term to capture unmodeled effects and correct the synthetic contrast images to better match experimental contrasts for arbitrary scan parameters. The physics inspired deep learning model implicitly accounts for some unmodeled physical effects occurring during the scan. As a proof of principle, we validate our approach on synthesizing arbitrary inversion recovery fast spin-echo scans using a commercially available 2D multi-contrast sequence. We observe that the proposed correction visually and numerically reduces the mismatch with experimentally collected contrasts compared to conventional synthetic MRI. Finally, we show results of a preliminary reader study and find that the proposed method statistically significantly improves in contrast and SNR as compared to synthetic MR images.
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Affiliation(s)
- Sidharth Kumar
- Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin 78712, TX, USA.
| | - Hamidreza Saber
- Dell Medical School Department of Neurology, The University of Texas at Austin, Austin 78712, TX, USA; Dell Medical School Department of Neurosurgery, The University of Texas at Austin, Austin 78712, TX, USA
| | - Odelin Charron
- Dell Medical School Department of Neurology, The University of Texas at Austin, Austin 78712, TX, USA
| | - Leorah Freeman
- Dell Medical School Department of Neurology, The University of Texas at Austin, Austin 78712, TX, USA; Dell Medical School Department of Diagnostic Medicine, The University of Texas at Austin, Austin 78712, TX, USA
| | - Jonathan I Tamir
- Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin 78712, TX, USA; Dell Medical School Department of Diagnostic Medicine, The University of Texas at Austin, Austin 78712, TX, USA; Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin 78712, TX, USA
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13
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Runderkamp BA, Roos T, van der Zwaag W, Strijkers GJ, Caan MWA, Nederveen AJ. Whole-liver flip-angle shimming at 7 T using parallel-transmit k T -point pulses and Fourier phase-encoded DREAM B 1 + mapping. Magn Reson Med 2024; 91:75-90. [PMID: 37799015 DOI: 10.1002/mrm.29819] [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: 03/16/2023] [Revised: 06/18/2023] [Accepted: 07/13/2023] [Indexed: 10/07/2023]
Abstract
PURPOSE To obtain homogeneous signal throughout the human liver at 7 T. Flip angle (FA) shimming in 7T whole-liver imaging was performed through parallel-transmit kT -point pulses based on subject-specific multichannel absoluteB 1 + $$ {\mathrm{B}}_1^{+} $$ maps from Fourier phase-encoded dual refocusing echo acquisition mode (PE-DREAM). METHODS The optimal number of Fourier phase-encoding steps for PE-DREAMB 1 + $$ {\mathrm{B}}_1^{+} $$ mapping was determined for a 7T eight-channel parallel-transmission system. FA shimming experiments were performed in the liver of 7 healthy subjects with varying body mass index. In these subjects, firstB 0 $$ {\mathrm{B}}_0 $$ shimming and Fourier PE-DREAMB 1 + $$ {\mathrm{B}}_1^{+} $$ mapping were performed. Subsequently, three small-flip-angle 3D gradient-echo scans were acquired, comparing a circularly polarized (CP) mode, a phase shim, and a kT -point pulse. Resulting homogeneity was assessed and compared with estimated FA maps and distributions. RESULTS Fourier PE-DREAM with 13 phase-encoding steps resulted in a good tradeoff betweenB 1 + $$ {\mathrm{B}}_1^{+} $$ accuracy and scan time. Lower coefficient of variation values (average [min-max] across subjects) of the estimated FA in the volume of interest were observed using kT -points (7.4 [6.6%-8.0%]), compared with phase shimming (18.8 [12.9%-23.4%], p < 0.001) and CP (43.2 [39.4%-47.1%], p < 0.001). kT -points delivered whole-liver images with the nominal FA and the highest degree of homogeneity. CP and phase shimming resulted in either inaccurate or imprecise FA distributions. Here, locations having suboptimal FA in the estimated FA maps corresponded to liver areas suffering from inconsistent signal intensity and T1 -weighting in the gradient-echo scans. CONCLUSION Homogeneous whole-liver 3D gradient-echo acquisitions at 7 T can be obtained with eight-channel kT -point pulses calculated based on subject-specific multichannel absolute Fourier PE-DREAMB 1 + $$ {\mathrm{B}}_1^{+} $$ maps.
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Affiliation(s)
- Bobby A Runderkamp
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands
| | - Thomas Roos
- Spinoza Centre for Neuroimaging, Royal Netherlands Academy for Arts and Sciences (KNAW), Amsterdam, the Netherlands
- High-Field Research Group, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Wietske van der Zwaag
- Spinoza Centre for Neuroimaging, Royal Netherlands Academy for Arts and Sciences (KNAW), Amsterdam, the Netherlands
- Computational and Cognitive Neuroscience and Neuroimaging, Netherlands Institute for Neuroscience, KNAW, Amsterdam, the Netherlands
| | - Gustav J Strijkers
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam Cardiovascular Sciences, Amsterdam, the Netherlands
| | - Matthan W A Caan
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam Cardiovascular Sciences, Amsterdam, the Netherlands
| | - Aart J Nederveen
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands
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14
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Li T, Wang J, Yang Y, Glide-Hurst CK, Wen N, Cai J. Multi-parametric MRI for radiotherapy simulation. Med Phys 2023; 50:5273-5293. [PMID: 36710376 PMCID: PMC10382603 DOI: 10.1002/mp.16256] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 09/10/2022] [Accepted: 12/06/2022] [Indexed: 01/31/2023] Open
Abstract
Magnetic resonance imaging (MRI) has become an important imaging modality in the field of radiotherapy (RT) in the past decade, especially with the development of various novel MRI and image-guidance techniques. In this review article, we will describe recent developments and discuss the applications of multi-parametric MRI (mpMRI) in RT simulation. In this review, mpMRI refers to a general and loose definition which includes various multi-contrast MRI techniques. Specifically, we will focus on the implementation, challenges, and future directions of mpMRI techniques for RT simulation.
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Affiliation(s)
- Tian Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Jihong Wang
- Department of Radiation Physics, Division of Radiation Oncology, MD Anderson Cancer Center, Houston, Texas, USA
| | - Yingli Yang
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong Univeristy School of Medicine, Shanghai, China
- SJTU-Ruijing-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Carri K Glide-Hurst
- Department of Radiation Oncology, University of Wisconsin, Madison, Wisconsin, USA
| | - Ning Wen
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong Univeristy School of Medicine, Shanghai, China
- SJTU-Ruijing-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- The Global Institute of Future Technology, Shanghai Jiaotong University, Shanghai, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
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15
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Sneag DB, Abel F, Potter HG, Fritz J, Koff MF, Chung CB, Pedoia V, Tan ET. MRI Advancements in Musculoskeletal Clinical and Research Practice. Radiology 2023; 308:e230531. [PMID: 37581501 PMCID: PMC10477516 DOI: 10.1148/radiol.230531] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 06/01/2023] [Accepted: 06/07/2023] [Indexed: 08/16/2023]
Abstract
Over the past decades, MRI has become increasingly important for diagnosing and longitudinally monitoring musculoskeletal disorders, with ongoing hardware and software improvements aiming to optimize image quality and speed. However, surging demand for musculoskeletal MRI and increased interest to provide more personalized care will necessitate a stronger emphasis on efficiency and specificity. Ongoing hardware developments include more powerful gradients, improvements in wide-bore magnet designs to maintain field homogeneity, and high-channel phased-array coils. There is also interest in low-field-strength magnets with inherently lower magnetic footprints and operational costs to accommodate global demand in middle- and low-income countries. Previous approaches to decrease acquisition times by means of conventional acceleration techniques (eg, parallel imaging or compressed sensing) are now largely overshadowed by deep learning reconstruction algorithms. It is expected that greater emphasis will be placed on improving synthetic MRI and MR fingerprinting approaches to shorten overall acquisition times while also addressing the demand of personalized care by simultaneously capturing microstructural information to provide greater detail of disease severity. Authors also anticipate increased research emphasis on metal artifact reduction techniques, bone imaging, and MR neurography to meet clinical needs.
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Affiliation(s)
- Darryl B. Sneag
- From the Department of Radiology and Imaging, Hospital for Special
Surgery, 535 E 70th St, New York, NY 10021 (D.B.S., F.A., H.G.P., M.F.K.,
E.T.T.); Department of Radiology, New York University Grossman School of
Medicine, New York, NY (J.F.); Department of Radiology, University of California
San Diego, La Jolla, Calif (C.B.C.); Radiology Service, Veterans Affairs San
Diego Healthcare System, La Jolla, Calif (C.B.C.); and Department of Radiology
and Biomedical Imaging, University of California San Francisco, San Francisco,
Calif (V.P.)
| | - Frederik Abel
- From the Department of Radiology and Imaging, Hospital for Special
Surgery, 535 E 70th St, New York, NY 10021 (D.B.S., F.A., H.G.P., M.F.K.,
E.T.T.); Department of Radiology, New York University Grossman School of
Medicine, New York, NY (J.F.); Department of Radiology, University of California
San Diego, La Jolla, Calif (C.B.C.); Radiology Service, Veterans Affairs San
Diego Healthcare System, La Jolla, Calif (C.B.C.); and Department of Radiology
and Biomedical Imaging, University of California San Francisco, San Francisco,
Calif (V.P.)
| | - Hollis G. Potter
- From the Department of Radiology and Imaging, Hospital for Special
Surgery, 535 E 70th St, New York, NY 10021 (D.B.S., F.A., H.G.P., M.F.K.,
E.T.T.); Department of Radiology, New York University Grossman School of
Medicine, New York, NY (J.F.); Department of Radiology, University of California
San Diego, La Jolla, Calif (C.B.C.); Radiology Service, Veterans Affairs San
Diego Healthcare System, La Jolla, Calif (C.B.C.); and Department of Radiology
and Biomedical Imaging, University of California San Francisco, San Francisco,
Calif (V.P.)
| | - Jan Fritz
- From the Department of Radiology and Imaging, Hospital for Special
Surgery, 535 E 70th St, New York, NY 10021 (D.B.S., F.A., H.G.P., M.F.K.,
E.T.T.); Department of Radiology, New York University Grossman School of
Medicine, New York, NY (J.F.); Department of Radiology, University of California
San Diego, La Jolla, Calif (C.B.C.); Radiology Service, Veterans Affairs San
Diego Healthcare System, La Jolla, Calif (C.B.C.); and Department of Radiology
and Biomedical Imaging, University of California San Francisco, San Francisco,
Calif (V.P.)
| | - Matthew F. Koff
- From the Department of Radiology and Imaging, Hospital for Special
Surgery, 535 E 70th St, New York, NY 10021 (D.B.S., F.A., H.G.P., M.F.K.,
E.T.T.); Department of Radiology, New York University Grossman School of
Medicine, New York, NY (J.F.); Department of Radiology, University of California
San Diego, La Jolla, Calif (C.B.C.); Radiology Service, Veterans Affairs San
Diego Healthcare System, La Jolla, Calif (C.B.C.); and Department of Radiology
and Biomedical Imaging, University of California San Francisco, San Francisco,
Calif (V.P.)
| | - Christine B. Chung
- From the Department of Radiology and Imaging, Hospital for Special
Surgery, 535 E 70th St, New York, NY 10021 (D.B.S., F.A., H.G.P., M.F.K.,
E.T.T.); Department of Radiology, New York University Grossman School of
Medicine, New York, NY (J.F.); Department of Radiology, University of California
San Diego, La Jolla, Calif (C.B.C.); Radiology Service, Veterans Affairs San
Diego Healthcare System, La Jolla, Calif (C.B.C.); and Department of Radiology
and Biomedical Imaging, University of California San Francisco, San Francisco,
Calif (V.P.)
| | - Valentina Pedoia
- From the Department of Radiology and Imaging, Hospital for Special
Surgery, 535 E 70th St, New York, NY 10021 (D.B.S., F.A., H.G.P., M.F.K.,
E.T.T.); Department of Radiology, New York University Grossman School of
Medicine, New York, NY (J.F.); Department of Radiology, University of California
San Diego, La Jolla, Calif (C.B.C.); Radiology Service, Veterans Affairs San
Diego Healthcare System, La Jolla, Calif (C.B.C.); and Department of Radiology
and Biomedical Imaging, University of California San Francisco, San Francisco,
Calif (V.P.)
| | - Ek T. Tan
- From the Department of Radiology and Imaging, Hospital for Special
Surgery, 535 E 70th St, New York, NY 10021 (D.B.S., F.A., H.G.P., M.F.K.,
E.T.T.); Department of Radiology, New York University Grossman School of
Medicine, New York, NY (J.F.); Department of Radiology, University of California
San Diego, La Jolla, Calif (C.B.C.); Radiology Service, Veterans Affairs San
Diego Healthcare System, La Jolla, Calif (C.B.C.); and Department of Radiology
and Biomedical Imaging, University of California San Francisco, San Francisco,
Calif (V.P.)
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Gaur S, Panda A, Fajardo JE, Hamilton J, Jiang Y, Gulani V. Magnetic Resonance Fingerprinting: A Review of Clinical Applications. Invest Radiol 2023; 58:561-577. [PMID: 37026802 PMCID: PMC10330487 DOI: 10.1097/rli.0000000000000975] [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] [Indexed: 04/08/2023]
Abstract
ABSTRACT Magnetic resonance fingerprinting (MRF) is an approach to quantitative magnetic resonance imaging that allows for efficient simultaneous measurements of multiple tissue properties, which are then used to create accurate and reproducible quantitative maps of these properties. As the technique has gained popularity, the extent of preclinical and clinical applications has vastly increased. The goal of this review is to provide an overview of currently investigated preclinical and clinical applications of MRF, as well as future directions. Topics covered include MRF in neuroimaging, neurovascular, prostate, liver, kidney, breast, abdominal quantitative imaging, cardiac, and musculoskeletal applications.
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Affiliation(s)
- Sonia Gaur
- Department of Radiology, Michigan Medicine, Ann Arbor, MI
| | - Ananya Panda
- All India Institute of Medical Sciences, Jodhpur, Rajasthan, India
| | | | - Jesse Hamilton
- Department of Radiology, Michigan Medicine, Ann Arbor, MI
| | - Yun Jiang
- Department of Radiology, Michigan Medicine, Ann Arbor, MI
| | - Vikas Gulani
- Department of Radiology, Michigan Medicine, Ann Arbor, MI
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17
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Kang B, Singh M, Park H, Heo HY. Only-train-once MR fingerprinting for B 0 and B 1 inhomogeneity correction in quantitative magnetization-transfer contrast. Magn Reson Med 2023; 90:90-102. [PMID: 36883726 PMCID: PMC10149616 DOI: 10.1002/mrm.29629] [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: 09/29/2022] [Revised: 02/15/2023] [Accepted: 02/16/2023] [Indexed: 03/09/2023]
Abstract
PURPOSE To develop a fast, deep-learning approach for quantitative magnetization-transfer contrast (MTC)-MR fingerprinting (MRF) that simultaneously estimates multiple tissue parameters and corrects the effects of B0 and B1 variations. METHODS An only-train-once recurrent neural network was designed to perform the fast tissue-parameter quantification for a large range of different MRF acquisition schedules. It enabled a dynamic scan-wise linear calibration of the scan parameters using the measured B0 and B1 maps, which allowed accurate, multiple-tissue parameter mapping. MRF images were acquired from 8 healthy volunteers at 3 T. Estimated parameter maps from the MRF images were used to synthesize the MTC reference signal (Zref ) through Bloch equations at multiple saturation power levels. RESULTS The B0 and B1 errors in MR fingerprints, if not corrected, would impair the tissue quantification and subsequently corrupt the synthesized MTC reference images. Bloch equation-based numerical phantom studies and synthetic MRI analysis demonstrated that the proposed approach could correctly estimate water and semisolid macromolecule parameters, even with severe B0 and B1 inhomogeneities. CONCLUSION The only-train-once deep-learning framework can improve the reconstruction accuracy of brain-tissue parameter maps and be further combined with any conventional MRF or CEST-MRF method.
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Affiliation(s)
- Beomgu Kang
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Guseong-dong, Yuseong-gu, Daejeon, Republic of Korea
- Divison of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Munendra Singh
- Divison of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA
| | - HyunWook Park
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Guseong-dong, Yuseong-gu, Daejeon, Republic of Korea
| | - Hye-Young Heo
- Divison of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA
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18
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Cartesian vs radial MR-STAT: An efficiency and robustness study. Magn Reson Imaging 2023; 99:7-19. [PMID: 36709010 DOI: 10.1016/j.mri.2023.01.017] [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: 08/29/2022] [Revised: 10/21/2022] [Accepted: 01/14/2023] [Indexed: 01/27/2023]
Abstract
MR Spin TomogrAphy in Time-domain ("MR-STAT") is quantitative MR technique in which multiple quantitative parameters are estimated from a single short scan by solving a large-scale non-linear optimization problem. In this work we extended the MR-STAT framework to non-Cartesian gradient trajectories. Cartesian MR-STAT and radial MR-STAT were compared in terms of time-efficiency and robustness in simulations, gel phantom measurements and in vivo measurements. In simulations, we observed that both Cartesian and radial MR-STAT are highly robust against undersampling. Radial MR-STAT does have a lower spatial encoding power because the outer corners of k-space are never sampled. However, especially in T2, this is compensated by a higher dynamic encoding power that comes from sampling the k-space center with each readout. In gel phantom measurements, Cartesian MR-STAT was observed to be robust against overfitting whereas radial MR-STAT suffered from high-frequency artefacts in the parameter maps at later iterations. These artefacts are hypothesized to be related to hardware imperfections and were (partially) suppressed with image filters. The time-efficiencies were higher for Cartesian MR-STAT in all vials. In-vivo, the radial reconstruction again suffered from overfitting artefacts. The robustness of Cartesian MR-STAT over the entire range of experiments may make it preferable in a clinical setting, despite radial MR-STAT resulting in a higher T1 time-efficiency in white matter.
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19
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Ostenson J, Robison RK, Brittain EL, Damon BM. Feasibility of joint mapping of triglyceride saturation and water longitudinal relaxation in a single breath hold applied to high fat-fraction adipose depots in the periclavicular anatomy. Magn Reson Imaging 2023; 99:58-66. [PMID: 36764629 PMCID: PMC10088071 DOI: 10.1016/j.mri.2023.02.001] [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: 11/18/2022] [Revised: 01/27/2023] [Accepted: 02/06/2023] [Indexed: 02/11/2023]
Abstract
INTRODUCTION Simultaneous mapping of triglyceride (TAG) saturation and tissue water relaxation may improve the characterization of the structure and function of anatomies with significant adipose tissue. While several groups have demonstrated in vivo TAG saturation imaging using MRI, joint mapping of relaxation and TAG saturation is understudied. Such mappings may avoid bias from physiological motion, if they can be done within a single breath-hold, and also account for static and applied magnetic field heterogeneity. METHODS We propose a transient-state/MR fingerprinting single breath-hold sequence at 3 T, a low-rank reconstruction, and a parameter estimation pipeline that jointly estimates the number of double bonds (NDB), number of methylene interrupted double bonds (NMIDB), and tissue water T1, while accounting for non-ideal radiofrequency transmit scaling and off-resonance effects. We test the proposed method in simulations, in phantom against MR spectroscopy (MRS), and in vivo regions in and around high fat fraction (FF) periclavicular adipose tissue. Partial volume and multi-peak transverse relaxation effects are explored. RESULTS The simulation results demonstrate accurate NDB, NMIDB, and water T1 estimates across a range of NDB, NMIDB, and T1 values. In phantoms, the proposed method's estimates of NDB and NMIDB correlate with those from MR spectroscopy (Pearson correlation ≥0.98), while the water T1 estimates are concordant with a standard phantom. The NDB and NMIDB are sensitive to partial volumes of water, showing increasing bias at FF < 40%. This bias is found to be due to noise and transverse relaxation effects. The in vivo periclavicular adipose tissue has high FF (>90%). The adipose tissue NDB and NMIDB, and muscle T1 estimates are comparable to those reported in the literature. CONCLUSION Robust estimation of NDB, NMIDB at high FF and water T1 across a broad range of FFs are feasible using the proposed methods. Further reduction of noise and model bias are needed to employ the proposed technique in low FF anatomies and pathologies.
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Affiliation(s)
- Jason Ostenson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States of America; Dept. of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America.
| | - Ryan K Robison
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States of America; Philips, Gainesville, FL, United States of America
| | - Evan L Brittain
- Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, TN, United States of America
| | - Bruce M Damon
- Dept. of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America; Carle Clinical Imaging Research Program, Urbana, IL, United States of America; Stephens Family Clinical Research Institute, Carle Health, Urbana, IL, United States of America; Department of Bioengineering and Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, United States of America; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States of America
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20
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Schmidt R, Keban E, Bollmann S, Wiggins CJ, Niendorf T. Scaling the mountains: what lies above 7 Tesla magnetic resonance? MAGMA (NEW YORK, N.Y.) 2023; 36:151-157. [PMID: 37072540 PMCID: PMC10140119 DOI: 10.1007/s10334-023-01087-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/27/2023] [Indexed: 04/20/2023]
Affiliation(s)
- Rita Schmidt
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Elena Keban
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, University of Bern, Bern, Switzerland
| | - Saskia Bollmann
- School of Information Technology and Electrical Engineering, Faculty of Engineering, Architecture and Information Technology, The University of Queensland, Brisbane, Australia
| | - Christopher J Wiggins
- Imaging Core Facility, Institute for Neurology and Medicine, Forschungszentrum Julich, Julich, Germany
| | - Thoralf Niendorf
- Berlin Ultrahigh Field Facility, Max-Delbrueck Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany.
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21
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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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22
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Eck BL, Yim M, Hamilton JI, da Cruz GJL, Li X, Flamm SD, Tang WHW, Prieto C, Seiberlich N, Kwon DH. Cardiac Magnetic Resonance Fingerprinting: Potential Clinical Applications. Curr Cardiol Rep 2023; 25:119-131. [PMID: 36805913 PMCID: PMC10134477 DOI: 10.1007/s11886-022-01836-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/10/2022] [Indexed: 02/21/2023]
Abstract
PURPOSE OF REVIEW Cardiac magnetic resonance fingerprinting (cMRF) has developed as a technique for rapid, multi-parametric tissue property mapping that has potential to both improve cardiac MRI exam efficiency and expand the information captured. In this review, we describe the cMRF technique, summarize technical developments and in vivo reports, and highlight potential clinical applications. RECENT FINDINGS Technical developments in cMRF continue to progress rapidly, including motion compensated reconstruction, additional tissue property quantification, signal time course analysis, and synthetic LGE image generation. Such technical developments can enable simplified CMR protocols by combining multiple evaluations into a single protocol and reducing the number of breath-held scans. cMRF continues to be reported for use in a range of pathologies; however barriers to clinical implementation remain. Technical developments are described in this review, followed by a focus on potential clinical applications that they may support. Clinical translation of cMRF could shorten protocols, improve CMR accessibility, and provide additional information as compared to conventional cardiac parametric mapping methods. Current needs for clinical implementation are discussed, as well as how those needs may be met in order to bring cMRF from its current research setting to become a viable tool for patient care.
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Affiliation(s)
- Brendan L Eck
- Imaging Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Michael Yim
- Heart, Vascular, and Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Jesse I Hamilton
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Gastao José Lima da Cruz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, England, UK
| | - Xiaojuan Li
- Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Scott D Flamm
- Heart, Vascular, and Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA
- Imaging Institute, Cleveland Clinic, Cleveland, OH, USA
| | - W H Wilson Tang
- Heart, Vascular, and Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, England, UK
- School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Nicole Seiberlich
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Deborah H Kwon
- Heart, Vascular, and Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA.
- Imaging Institute, Cleveland Clinic, Cleveland, OH, USA.
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23
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Eck BL, Yang M, Elias JJ, Winalski CS, Altahawi F, Subhas N, Li X. Quantitative MRI for Evaluation of Musculoskeletal Disease: Cartilage and Muscle Composition, Joint Inflammation, and Biomechanics in Osteoarthritis. Invest Radiol 2023; 58:60-75. [PMID: 36165880 PMCID: PMC10198374 DOI: 10.1097/rli.0000000000000909] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
ABSTRACT Magnetic resonance imaging (MRI) is a valuable tool for evaluating musculoskeletal disease as it offers a range of image contrasts that are sensitive to underlying tissue biochemical composition and microstructure. Although MRI has the ability to provide high-resolution, information-rich images suitable for musculoskeletal applications, most MRI utilization remains in qualitative evaluation. Quantitative MRI (qMRI) provides additional value beyond qualitative assessment via objective metrics that can support disease characterization, disease progression monitoring, or therapy response. In this review, musculoskeletal qMRI techniques are summarized with a focus on techniques developed for osteoarthritis evaluation. Cartilage compositional MRI methods are described with a detailed discussion on relaxometric mapping (T 2 , T 2 *, T 1ρ ) without contrast agents. Methods to assess inflammation are described, including perfusion imaging, volume and signal changes, contrast-enhanced T 1 mapping, and semiquantitative scoring systems. Quantitative characterization of structure and function by bone shape modeling and joint kinematics are described. Muscle evaluation by qMRI is discussed, including size (area, volume), relaxometric mapping (T 1 , T 2 , T 1ρ ), fat fraction quantification, diffusion imaging, and metabolic assessment by 31 P-MR and creatine chemical exchange saturation transfer. Other notable technologies to support qMRI in musculoskeletal evaluation are described, including magnetic resonance fingerprinting, ultrashort echo time imaging, ultrahigh-field MRI, and hybrid MRI-positron emission tomography. Challenges for adopting and using qMRI in musculoskeletal evaluation are discussed, including the need for metal artifact suppression and qMRI standardization.
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Affiliation(s)
- Brendan L. Eck
- Program of Advanced Musculoskeletal Imaging, Cleveland Clinic, Cleveland, OH, USA
- Imaging Instute, Cleveland Clinic, Cleveland, OH, USA
| | - Mingrui Yang
- Program of Advanced Musculoskeletal Imaging, Cleveland Clinic, Cleveland, OH, USA
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - John J. Elias
- Program of Advanced Musculoskeletal Imaging, Cleveland Clinic, Cleveland, OH, USA
- Department of Research, Cleveland Clinic Akron General, Akron, OH, USA
| | - Carl S. Winalski
- Program of Advanced Musculoskeletal Imaging, Cleveland Clinic, Cleveland, OH, USA
- Imaging Instute, Cleveland Clinic, Cleveland, OH, USA
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Faysal Altahawi
- Program of Advanced Musculoskeletal Imaging, Cleveland Clinic, Cleveland, OH, USA
- Imaging Instute, Cleveland Clinic, Cleveland, OH, USA
| | - Naveen Subhas
- Program of Advanced Musculoskeletal Imaging, Cleveland Clinic, Cleveland, OH, USA
- Imaging Instute, Cleveland Clinic, Cleveland, OH, USA
| | - Xiaojuan Li
- Program of Advanced Musculoskeletal Imaging, Cleveland Clinic, Cleveland, OH, USA
- Imaging Instute, Cleveland Clinic, Cleveland, OH, USA
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
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24
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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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25
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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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26
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Seginer A, Schmidt R. Phase-based fast 3D high-resolution quantitative T 2 MRI in 7 T human brain imaging. Sci Rep 2022; 12:14088. [PMID: 35982143 PMCID: PMC9388657 DOI: 10.1038/s41598-022-17607-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 07/28/2022] [Indexed: 12/04/2022] Open
Abstract
Magnetic resonance imaging (MRI) is a powerful and versatile technique that offers a range of physiological, diagnostic, structural, and functional measurements. One of the most widely used basic contrasts in MRI diagnostics is transverse relaxation time (T2)-weighted imaging, but it provides only qualitative information. Realizing quantitative high-resolution T2 mapping is imperative for the development of personalized medicine, as it can enable the characterization of diseases progression. While ultra-high-field (≥ 7 T) MRI offers the means to gain new insights by increasing the spatial resolution, implementing fast quantitative T2 mapping cannot be achieved without overcoming the increased power deposition and radio frequency (RF) field inhomogeneity at ultra-high-fields. A recent study has demonstrated a new phase-based T2 mapping approach based on fast steady-state acquisitions. We extend this new approach to ultra-high field MRI, achieving quantitative high-resolution 3D T2 mapping at 7 T while addressing RF field inhomogeneity and utilizing low flip angle pulses; overcoming two main ultra-high field challenges. The method is based on controlling the coherent transverse magnetization in a steady-state gradient echo acquisition; achieved by utilizing low flip angles, a specific phase increment for the RF pulses, and short repetition times. This approach simultaneously extracts both T2 and RF field maps from the phase of the signal. Prior to in vivo experiments, the method was assessed using a 3D head-shaped phantom that was designed to model the RF field distribution in the brain. Our approach delivers fast 3D whole brain images with submillimeter resolution without requiring special hardware, such as multi-channel transmit coil, thus promoting high usability of the ultra-high field MRI in clinical practice.
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Affiliation(s)
| | - Rita Schmidt
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel. .,The Azrieli National Institute for Human Brain Imaging and Research, Weizmann Institute of Science, Rehovot, Israel.
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27
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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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28
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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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29
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Li Z, Xu X, Yang Y, Feng L. Repeatability and robustness of MP-GRASP T 1 mapping. Magn Reson Med 2022; 87:2271-2286. [PMID: 34971467 PMCID: PMC10061203 DOI: 10.1002/mrm.29131] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 12/03/2021] [Accepted: 12/06/2021] [Indexed: 12/14/2022]
Abstract
PURPOSE To demonstrate the repeatability of fast 3D T1 mapping using Magnetization-Prepared Golden-angle RAdial Sparse Parallel (MP-GRASP) MRI and its robustness to variation of imaging parameters including flip angle and spatial resolution in phantoms and the brain. THEORY AND METHODS Multiple imaging experiments were performed to (1) assess the robustness of MP-GRASP T1 mapping to B1 inhomogeneity using a single tube phantom filled with uniform MnCl2 liquid; (2) compare the repeatability of T1 mapping between MP-GRASP and inversion recovery-based spin-echo (IR-SE; over 12 scans), using a commercial T1MES phantom; (3) evaluate the longitudinal variation of T1 estimation using MP-GRASP with varying imaging parameters, including spatial resolution, flip angle, TR/TE, and acceleration rate, using the T1MES phantom (106 scans performed over a period of 12 months); and (4) evaluate the variation of T1 estimation using MP-GRASP with varying imaging parameters in the brain (24 scans in a single visit). In addition, the accuracy of MP-GRASP T1 mapping was also validated against IR-SE by performing linear correlation and calculating the Lin's concordance correlation coefficient (CCC). RESULTS MP-GRASP demonstrates good robustness to B1 inhomogeneity, with intra-slice variability below 1% in the single tube phantom experiment. The longitudinal variability is good both in the phantom (below 2.5%) and in the brain (below 2%) with varying imaging parameters. The T1 values estimated from MP-GRASP are accurate compared to that from the IR-SE imaging (R2 = 0.997, Lin's CCC = 0.996). CONCLUSION MP-GRASP shows excellent repeatability of T1 estimation over time, and it is also robust to variation of different imaging parameters evaluated in this study.
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Affiliation(s)
- Zhitao Li
- Department of Radiology, Stanford University, Palo Alto, California, United States
| | - Xiang Xu
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Yang Yang
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Li Feng
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
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30
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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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31
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Cao P, Wang Z, Liu C, Li T, Hui E, Cai J. Motion-resolved and free-breathing liver MRF. Magn Reson Imaging 2022; 91:69-80. [DOI: 10.1016/j.mri.2022.05.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 05/01/2022] [Accepted: 05/22/2022] [Indexed: 11/28/2022]
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32
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Wang F, Dong Z, Reese TG, Rosen B, Wald LL, Setsompop K. 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] [MESH Headings] [Grants] [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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Affiliation(s)
- Fuyixue Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA; Harvard-MIT Health Sciences and Technology, MIT, Cambridge, Massachusetts, USA; Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA.
| | - Zijing Dong
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA; Electrical Engineering and Computer Science, MIT, Cambridge, Massachusetts, USA
| | - Timothy G Reese
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA; Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Bruce Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA; Harvard-MIT Health Sciences and Technology, MIT, Cambridge, Massachusetts, USA; Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Lawrence L Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA; Harvard-MIT Health Sciences and Technology, MIT, Cambridge, Massachusetts, USA; Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Kawin Setsompop
- Department of Radiology, Stanford University, Stanford, USA; Department of Electrical Engineering, Stanford University, Stanford, USA
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33
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Barbieri M, Lee PK, Brizi L, Giampieri E, Solera F, Castellani G, Hargreaves BA, Testa C, Lodi R, Remondini D. Circumventing the curse of dimensionality in magnetic resonance fingerprinting through a deep learning approach. NMR IN BIOMEDICINE 2022; 35:e4670. [PMID: 35088466 DOI: 10.1002/nbm.4670] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 11/15/2021] [Accepted: 12/02/2021] [Indexed: 06/14/2023]
Abstract
Magnetic resonance fingerprinting (MRF) is a rapidly developing approach for fast quantitative MRI. A typical drawback of dictionary-based MRF is an explosion of the dictionary size as a function of the number of reconstructed parameters, according to the "curse of dimensionality", which determines an explosion of resource requirements. Neural networks (NNs) have been proposed as a feasible alternative, but this approach is still in its infancy. In this work, we design a deep learning approach to MRF using a fully connected network (FCN). In the first part we investigate, by means of simulations, how the NN performance scales with the number of parameters to be retrieved in comparison with the standard dictionary approach. Four MRF sequences were considered: IR-FISP, bSSFP, IR-FISP-B1 , and IR-bSSFP-B1 , the latter two designed to be more specific for B1+ parameter encoding. Estimation accuracy, memory usage, and computational time required to perform the estimation task were considered to compare the scalability capabilities of the dictionary-based and the NN approaches. In the second part we study optimal training procedures by including different data augmentation and preprocessing strategies during training to achieve better accuracy and robustness to noise and undersampling artifacts. The study is conducted using the IR-FISP MRF sequence exploiting both simulations and in vivo acquisitions. Results demonstrate that the NN approach outperforms the dictionary-based approach in terms of scalability capabilities. Results also allow us to heuristically determine the optimal training strategy to make an FCN able to predict T1 , T2 , and M0 maps that are in good agreement with those obtained with the original dictionary approach. k-SVD denoising is proposed and found to be critical as a preprocessing step to handle undersampled data.
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Affiliation(s)
- Marco Barbieri
- Department of Physics and Astronomy "Augusto Righi", University of Bologna, Bologna, Italy
- Department of Radiology, Stanford University, California, United States
| | - Philip K Lee
- Department of Electrical Engineering, Stanford University, California, United States
| | - Leonardo Brizi
- Department of Physics and Astronomy "Augusto Righi", University of Bologna, Bologna, Italy
- INFN, Sezione di Bologna, Bologna, Italy
| | - Enrico Giampieri
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy
| | | | - Gastone Castellani
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy
| | - Brian A Hargreaves
- Department of Radiology, Stanford University, California, United States
- Department of Electrical Engineering, Stanford University, California, United States
- Department of Bioengineering, Stanford University, California, United States
| | - Claudia Testa
- Department of Physics and Astronomy "Augusto Righi", University of Bologna, Bologna, Italy
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Functional and Molecular Neuroimaging Unit, Bologna, Italy
| | - Raffaele Lodi
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Functional and Molecular Neuroimaging Unit, Bologna, Italy
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Daniel Remondini
- Department of Physics and Astronomy "Augusto Righi", University of Bologna, Bologna, Italy
- INFN, Sezione di Bologna, Bologna, Italy
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34
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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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35
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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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36
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Scope Crafts E, Lu H, Ye H, Wald LL, Zhao B. An efficient approach to optimal experimental design for magnetic resonance fingerprinting with B‐splines. Magn Reson Med 2022; 88:239-253. [DOI: 10.1002/mrm.29212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 01/30/2022] [Accepted: 02/08/2022] [Indexed: 11/05/2022]
Affiliation(s)
- Evan Scope Crafts
- Oden Institute for Computational Engineering and Sciences University of Texas at Austin Austin Texas USA
| | - Hengfa Lu
- Department of Biomedical Engineering University of Texas at Austin Austin Texas USA
| | - Huihui Ye
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering Zhejiang University Hangzhou Zhejiang China
- Center for Brain Imaging Science and Technology Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University Hangzhou Zhejiang China
| | - Lawrence L. Wald
- Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown Massachusetts USA
- Department of Radiology Harvard Medical School Boston Massachusetts USA
- Harvard‐MIT Division of Health Sciences and Technology Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Bo Zhao
- Oden Institute for Computational Engineering and Sciences University of Texas at Austin Austin Texas USA
- Department of Biomedical Engineering University of Texas at Austin Austin Texas USA
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37
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Perlman O, Farrar CT, Heo HY. MR fingerprinting for semisolid magnetization transfer and chemical exchange saturation transfer quantification. NMR IN BIOMEDICINE 2022; 36:e4710. [PMID: 35141967 PMCID: PMC9808671 DOI: 10.1002/nbm.4710] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Revised: 01/18/2022] [Accepted: 02/04/2022] [Indexed: 05/11/2023]
Abstract
Chemical exchange saturation transfer (CEST) MRI has positioned itself as a promising contrast mechanism, capable of providing molecular information at sufficient resolution and amplified sensitivity. However, it has not yet become a routinely employed clinical technique, due to a variety of confounding factors affecting its contrast-weighted image interpretation and the inherently long scan time. CEST MR fingerprinting (MRF) is a novel approach for addressing these challenges, allowing simultaneous quantitation of several proton exchange parameters using rapid acquisition schemes. Recently, a number of deep-learning algorithms have been developed to further boost the performance and speed of CEST and semi-solid macromolecule magnetization transfer (MT) MRF. This review article describes the fundamental theory behind semisolid MT/CEST-MRF and its main applications. It then details supervised and unsupervised learning approaches for MRF image reconstruction and describes artificial intelligence (AI)-based pipelines for protocol optimization. Finally, practical considerations are discussed, and future perspectives are given, accompanied by basic demonstration code and data.
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Affiliation(s)
- Or Perlman
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Christian T. Farrar
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Hye-Young Heo
- Division of MR Research, Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
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38
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Gu Y, Wang L, Yang H, Wu Y, Kim K, Zhu Y, Androjna C, Zhu X, Chen Y, Zhong K, Yu X. Three-dimensional high-resolution T 1 and T 2 mapping of whole macaque brain at 9.4 T using magnetic resonance fingerprinting. Magn Reson Med 2022; 87:2901-2913. [PMID: 35129226 DOI: 10.1002/mrm.29181] [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: 12/08/2021] [Revised: 01/10/2022] [Accepted: 01/10/2022] [Indexed: 11/12/2022]
Abstract
PURPOSE Quantitative T1 and T2 mapping in non-human primates with whole-brain coverage is challenged by the requirement of sub-millimeter resolution and the inhomogeneity of the transmit magnetic field (B1 + ) covering a large field of view. The goal of the current study is to develop a magnetic resonance fingerprinting (MRF) method for simultaneous T1 and T2 mapping of the entire macaque brain within feasible scan time. METHODS A three-dimensional (3D) MRF sequence with both inversion- and T2 -preparation modules was developed and evaluated on a 9.4 T preclinical scanner. Data acquisition used a 3D stack-of-spirals trajectory, with undersampling along both the in-plane and the through-plane directions. The effect of B1 + inhomogeneity was accounted for by matching the acquired fingerprint to a dictionary simulated with the B1 + factors measured from a separate scan. In vitro and ex vivo studies were performed to evaluate the accuracy and the undersampling capacity of the MRF method. The application of the MRF method for in vivo, brain-wide T1 and T2 mapping was demonstrated on macaques at 4, 6, and 12 years of age. RESULTS The MRF method enabled highly repeatable T1 and T2 mapping at high spatial resolution (0.35 × 0.35 × 1 mm3 ) with an acceleration factor of 24. In vivo studies showed significant age-related T2 reduction in deep gray nuclei including the globus pallidus, the putamen, and the caudate nucleus. CONCLUSIONS This study demonstrates the first MRF study for brain-wide, multi-parametric quantification in non-human primates with sub-millimeter resolution.
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Affiliation(s)
- Yuning Gu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Lulu Wang
- High Magnetic Field Laboratory, Chinese Academy of Sciences, Hefei, China.,Anhui Province Key Laboratory of High Field Magnetic Resonance Imaging, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China
| | - Hongyi Yang
- High Magnetic Field Laboratory, Chinese Academy of Sciences, Hefei, China.,Anhui Province Key Laboratory of High Field Magnetic Resonance Imaging, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.,School of Graduate Studies, Science Island Branch, University of Science and Technology of China, Hefei, China
| | - Yun Wu
- High Magnetic Field Laboratory, Chinese Academy of Sciences, Hefei, China.,Anhui Province Key Laboratory of High Field Magnetic Resonance Imaging, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China
| | - Kihwan Kim
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Yuran Zhu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Charlie Androjna
- Center for Preclinical Magnetic Resonance Imaging, Cleveland Clinic Foundation, Cleveland, Ohio, USA
| | - Xiaofeng Zhu
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, USA
| | - Yong Chen
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Kai Zhong
- High Magnetic Field Laboratory, Chinese Academy of Sciences, Hefei, China.,Anhui Province Key Laboratory of High Field Magnetic Resonance Imaging, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.,Biomedical Engineering Department, Peking University, Beijing, China
| | - Xin Yu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.,Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
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Feasibility of Magnetic Resonance Fingerprinting on Aging MRI Hardware. Tomography 2021; 8:10-21. [PMID: 35076600 PMCID: PMC8788417 DOI: 10.3390/tomography8010002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 12/15/2021] [Accepted: 12/17/2021] [Indexed: 11/17/2022] Open
Abstract
The purpose of this work is to evaluate the feasibility of performing magnetic resonance fingerprinting (MRF) on older and lower-performance MRI hardware as a means to bring advanced imaging to the aging MRI install base. Phantom and in vivo experiments were performed on a 1.5T Siemens Aera (installed 2015) and 1.5T Siemens Symphony (installed 2002). A 2D spiral MRF sequence for simultaneous T1/T2/M0 mapping was implemented on both scanners with different gradient trajectories to accommodate system specifications. In phantom, for T1/T2 values in a physiologically relevant range (T1: 195-1539 ms; T2: 20-267 ms), scanners had strong correlation (R2 > 0.999) with average absolute percent difference of 8.1% and 10.1%, respectively. Comparison of the two trajectories on the newer scanner showed differences of 2.6% (T1) and 10.9% (T2), suggesting a partial explanation of the observed inter-scanner bias. Inter-scanner agreement was better when the same trajectory was used, with differences of 6.0% (T1) and 4.0% (T2). Intra-scanner coefficient of variation (CV) of T1 and T2 estimates in phantom were <2.0% and in vivo were ≤3.5%. In vivo inter-scanner white matter CV was 4.8% (T1) and 5.1% (T2). White matter measurements on the aging scanner after two months were consistent, with differences of 1.9% (T1) and 3.9% (T2). In conclusion, MRF is feasible on an aging MRI scanner and required only changes to the gradient trajectory.
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40
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Wang B, Zhang B, Yu Z, Ianniello C, Lakshmanan K, Paska J, Madelin G, Cloos M, Brown R. A radially interleaved sodium and proton coil array for brain MRI at 7 T. NMR IN BIOMEDICINE 2021; 34:e4608. [PMID: 34476861 PMCID: PMC9362999 DOI: 10.1002/nbm.4608] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Revised: 08/09/2021] [Accepted: 08/11/2021] [Indexed: 06/13/2023]
Abstract
The objective of the current study was to design and build a dual-tuned coil array for simultaneous 23 Na/1 H MRI of the human brain at 7 T. Quality factor, experimental B1+ measurements, and electromagnetic simulations in prototypes showed that setups consisting of geometrically interleaved 1 H and 23 Na loops performed better than or similar to 1 H or 23 Na loops in isolation. Based on these preliminary findings, we built a transmit/receive eight-channel 23 Na loop array that was geometrically interleaved with a transmit/receive eight-channel 1 H loop array. We assessed the performance of the manufactured array with mononuclear signal-to-noise ratio (SNR) and B1+ measurements, along with multinuclear magnetic resonance fingerprinting maps and images. The 23 Na array within the developed dual-tuned device provided more than 50% gain in peripheral SNR and similar B1+ uniformity and coverage as a reference birdcage coil of similar size. The 1 H array provided good B1+ uniformity in the brain, excluding the cerebellum and brain stem. The integrated 23 Na and 1 H arrays were used to demonstrate truly simultaneous quantitative 1 H mapping and 23 Na imaging.
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Affiliation(s)
- Bili Wang
- 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
| | - Bei Zhang
- 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
- Advanced Imaging Research Center, Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Zidan Yu
- 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
- Vilcek Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, New York, USA
| | - Carlotta Ianniello
- 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
- Vilcek Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, New York, USA
| | - Karthik Lakshmanan
- 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
| | - Jan Paska
- 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
| | - Guillaume Madelin
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Vilcek Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, New York, USA
| | - Martijn Cloos
- 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
- Vilcek Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, New York, USA
- Centre for Advanced Imaging, The University of Queensland, St Lucia, Brisbane, Queensland, Australia
| | - Ryan Brown
- 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
- Vilcek Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, New York, USA
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Moinian S, Vegh V, O'Brien K, Reutens D. Magnetic resonance fingerprinting residual signals can disassociate human grey matter regions. Brain Struct Funct 2021; 227:313-329. [PMID: 34697684 DOI: 10.1007/s00429-021-02402-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 09/29/2021] [Indexed: 10/20/2022]
Abstract
The importance of accurate structural discrimination of the human grey matter regions has motivated the development of observer-independent reproducible methods that account for inter-individual architectonic variations. We introduce a non-invasive statistical residual analysis framework, employing unique tissue-specific magnetic resonance fingerprinting (MRF) signals after adjusting for the effect of T1 and T2* MR relaxometry parameters (here termed MRF residuals). A 7 T Siemens MR scanner was used to acquire MRF signals, quantitative transmit magnetic field (B1+) maps and T1-weighted anatomical images of eleven cortical areas (5L, 5M, 5Ci, 7A, 7P, 7PC, hIP3, BA2, BA4a, BA4p and BA6) from six female participants. MRF residual signal for each voxel was calculated as the difference between the actual and best matching MRF signal evolutions from a precomputed MRF dictionary covering a range of T1, T2* and B1+ values. To compare MRF residuals between regions of interest, normalised autocorrelation was used as a shape-based statistical signal characterisation method and the Euclidean distance between autocorrelation profiles of residuals was used to measure the interareal dissimilarity. In the eleven cortical areas in both cerebral hemispheres of six participants, the proposed MRF residual analysis consistently showed interareal dissimilarity profiles that concorded with histological studies, indicating that MRF residuals potentially contain tissue microstructural information. MRF residual signals provide additional area-specific information that is complementary to the MR relaxometry-based (T1, T2*) information used previously for distinguishing microstructural differences between human cerebral cortex regions in vivo. The proposed approach led to more accurate identification of structural variations across cortical areas of interest.
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Affiliation(s)
- Shahrzad Moinian
- Centre for Advanced Imaging, The University of Queensland, Building 57, Research Road, Brisbane, QLD, 4072, Australia.,ARC Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Building 57, Research Road, Brisbane, QLD, 4072, Australia
| | - Viktor Vegh
- Centre for Advanced Imaging, The University of Queensland, Building 57, Research Road, Brisbane, QLD, 4072, Australia.,ARC Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Building 57, Research Road, Brisbane, QLD, 4072, Australia
| | - Kieran O'Brien
- Centre for Advanced Imaging, The University of Queensland, Building 57, Research Road, Brisbane, QLD, 4072, Australia.,ARC Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Building 57, Research Road, Brisbane, QLD, 4072, Australia.,Siemens Healthcare Pty Ltd, 153 Campbell Street, Brisbane, QLD, 4006, Australia
| | - David Reutens
- Centre for Advanced Imaging, The University of Queensland, Building 57, Research Road, Brisbane, QLD, 4072, Australia. .,ARC Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Building 57, Research Road, Brisbane, QLD, 4072, Australia.
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Chen Y, Lu L, Zhu T, Ma D. Technical overview of magnetic resonance fingerprinting and its applications in radiation therapy. Med Phys 2021; 49:2846-2860. [PMID: 34633687 DOI: 10.1002/mp.15254] [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: 08/01/2021] [Revised: 08/23/2021] [Accepted: 09/17/2021] [Indexed: 11/07/2022] Open
Abstract
Magnetic resonance fingerprinting (MRF) is an emerging imaging technique for rapid and simultaneous quantification of multiple tissue properties. The technique has been developed for quantitative imaging of different organs. The obtained quantitative measures have the potential to improve multiple steps of a typical radiotherapy workflow and potentially further improve integration of magnetic resonance imaging guided clinical decision making. In this review paper, we first provide a technical overview of the MRF method from data acquisition to postprocessing, along with recent development in advanced reconstruction methods. We further discuss critical aspects that could influence its usage in radiation therapy, such as accuracy and precision, repeatability and reproducibility, geometric distortion, and motion robustness. Finally, future directions for MRF application in radiation therapy are discussed.
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Affiliation(s)
- Yong Chen
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Lan Lu
- Radiation Oncology, Cleveland Clinic, Cleveland, Ohio, USA
| | - Tong Zhu
- Radiation Oncology, Washington University in St Louis, St Louis, Missouri, USA
| | - Dan Ma
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
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Jordan SP, Hu S, Rozada I, McGivney DF, Boyacioğlu R, Jacob DC, Huang S, Beverland M, Katzgraber HG, Troyer M, Griswold MA, Ma D. Automated design of pulse sequences for magnetic resonance fingerprinting using physics-inspired optimization. Proc Natl Acad Sci U S A 2021; 118:e2020516118. [PMID: 34593630 PMCID: PMC8501900 DOI: 10.1073/pnas.2020516118] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/18/2021] [Indexed: 11/18/2022] Open
Abstract
Magnetic resonance fingerprinting (MRF) is a method to extract quantitative tissue properties such as [Formula: see text] and [Formula: see text] relaxation rates from arbitrary pulse sequences using conventional MRI hardware. MRF pulse sequences have thousands of tunable parameters, which can be chosen to maximize precision and minimize scan time. Here, we perform de novo automated design of MRF pulse sequences by applying physics-inspired optimization heuristics. Our experimental data suggest that systematic errors dominate over random errors in MRF scans under clinically relevant conditions of high undersampling. Thus, in contrast to prior optimization efforts, which focused on statistical error models, we use a cost function based on explicit first-principles simulation of systematic errors arising from Fourier undersampling and phase variation. The resulting pulse sequences display features qualitatively different from previously used MRF pulse sequences and achieve fourfold shorter scan time than prior human-designed sequences of equivalent precision in [Formula: see text] and [Formula: see text] Furthermore, the optimization algorithm has discovered the existence of MRF pulse sequences with intrinsic robustness against shading artifacts due to phase variation.
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Affiliation(s)
| | - Siyuan Hu
- Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106
| | - Ignacio Rozada
- Optimization Solutions, 1QBit, Vancouver, BC V6E 4B1, Canada
| | - Debra F McGivney
- Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106
| | - Rasim Boyacioğlu
- Radiology Department, Case Western Reserve University, Cleveland, OH 44106
| | - Darryl C Jacob
- Department of Physics and Astronomy, Texas A & M University, College Station, TX 77843
| | - Sherry Huang
- Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106
| | | | | | | | - Mark A Griswold
- Radiology Department, Case Western Reserve University, Cleveland, OH 44106
| | - Dan Ma
- Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106;
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Wang M, Perucho JAU, Cao P, Vardhanabhuti V, Cui D, Wang Y, Khong PL, Hui ES, Lee EYP. Repeatability of MR fingerprinting in normal cervix and utility in cervical carcinoma. Quant Imaging Med Surg 2021; 11:3990-4003. [PMID: 34476184 DOI: 10.21037/qims-20-1382] [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: 12/21/2020] [Accepted: 04/08/2021] [Indexed: 11/06/2022]
Abstract
Background Magnetic resonance fingerprinting (MRF) is a fast-imaging acquisition technique that generates quantitative and co-registered parametric maps. The aim of this feasibility study was to evaluate the agreement between MRF and phantom reference values, scan-rescan repeatability of MRF in normal cervix, and its ability to distinguish cervical carcinoma (CC) from normal cervical tissues. Methods An International Society of Magnetic Resonance in Medicine/National Institute of Standards and Technology (ISMRM/NIST) phantom was scanned using MRF 15 times over 65 days. Agreement between MRF and phantom reference T1 and T2 values was assessed by linear regression. Healthy volunteers and patients with suspected CC were prospectively recruited. MRF was repeated twice for healthy volunteers (MRF1 and MRF2). Volumes of interest of normal cervical tissues and CC were delineated on T1 and T2 maps. MRF scan-rescan repeatability was evaluated by Bland-Altman plots, within-subject coefficients of variation (wCV), and intraclass correlation coefficients (ICC). T1 and T2 values were compared between CC and normal cervical tissues using Mann-Whitney U test. Receiver operating characteristic (ROC) analysis was performed to evaluate diagnostic efficiency. Results Strong correlations were observed between MRF and phantom (R2=0.999 for T1, 0.981 for T2). Twelve healthy volunteers (28.7±5.1 years) and 28 patients with CC (54.6±15.2 years) were recruited for the in-vivo experiments. Repeatability of MRF parameters were wCV <3% for T1, <5% for T2 and ICC ≥0.92 for T1, ≥0.94 for T2. T1 value of CC (1,529±112 ms) was higher than normal mucosa [MRF1: 1,430±129 ms, MRF2: 1,440±130 ms; P=0.031, area under the curve (AUC) ≥0.717] and normal stroma (MRF1: 1,258±101 ms, MRF2: 1,276±105 ms; P<0.001, AUC ≥0.946). T2 value of CC (69±9 ms) was lower than normal mucosa (MRF1: 88±16 ms, MRF2: 87±13 ms; P<0.001, AUC ≥0.854), but was not different from normal stroma (P=0.919). Conclusions Excellent agreement was observed between MRF and phantom reference values. MRF exhibited excellent scan-rescan repeatability in normal cervix with potential value in differentiating CC from normal cervical tissues.
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Affiliation(s)
- Mandi Wang
- Department of Diagnostic Radiology, Queen Mary Hospital, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Jose A U Perucho
- Department of Diagnostic Radiology, Queen Mary Hospital, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Peng Cao
- Department of Diagnostic Radiology, Queen Mary Hospital, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Varut Vardhanabhuti
- Department of Diagnostic Radiology, Queen Mary Hospital, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Di Cui
- Department of Diagnostic Radiology, Queen Mary Hospital, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Yiang Wang
- Department of Diagnostic Radiology, Queen Mary Hospital, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Pek-Lan Khong
- Department of Diagnostic Radiology, Queen Mary Hospital, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Edward S Hui
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, China
| | - Elaine Y P Lee
- Department of Diagnostic Radiology, Queen Mary Hospital, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
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A deep learning approach for magnetic resonance fingerprinting: Scaling capabilities and good training practices investigated by simulations. Phys Med 2021; 89:80-92. [PMID: 34352679 DOI: 10.1016/j.ejmp.2021.07.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 07/12/2021] [Accepted: 07/13/2021] [Indexed: 11/22/2022] Open
Abstract
MR fingerprinting (MRF) is an innovative approach to quantitative MRI. A typical disadvantage of dictionary-based MRF is the explosive growth of the dictionary as a function of the number of reconstructed parameters, an instance of the curse of dimensionality, which determines an explosion of resource requirements. In this work, we describe a deep learning approach for MRF parameter map reconstruction using a fully connected architecture. Employing simulations, we have investigated how the performance of the Neural Networks (NN) approach scales with the number of parameters to be retrieved, compared to the standard dictionary approach. We have also studied optimal training procedures by comparing different strategies for noise addition and parameter space sampling, to achieve better accuracy and robustness to noise. Four MRF sequences were considered: IR-FISP, bSSFP, IR-FISP-B1, and IR-bSSFP-B1. A comparison between NN and the dictionary approaches in reconstructing parameter maps as a function of the number of parameters to be retrieved was performed using a numerical brain phantom. Results demonstrated that training with random sampling and different levels of noise variance yielded the best performance. NN performance was at least as good as the dictionary-based approach in reconstructing parameter maps using Gaussian noise as a source of artifacts: the difference in performance increased with the number of estimated parameters because the dictionary method suffers from the coarse resolution of the parameter space sampling. The NN proved to be more efficient in memory usage and computational burden, and has great potential for solving large-scale MRF problems.
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Feng L, Liu F, Soultanidis G, Liu C, Benkert T, Block KT, Fayad ZA, Yang Y. Magnetization-prepared GRASP MRI for rapid 3D T1 mapping and fat/water-separated T1 mapping. Magn Reson Med 2021; 86:97-114. [PMID: 33580909 PMCID: PMC8197608 DOI: 10.1002/mrm.28679] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 12/18/2020] [Accepted: 12/22/2020] [Indexed: 12/22/2022]
Abstract
PURPOSE This study aimed to (i) develop Magnetization-Prepared Golden-angle RAdial Sparse Parallel (MP-GRASP) MRI using a stack-of-stars trajectory for rapid free-breathing T1 mapping and (ii) extend MP-GRASP to multi-echo acquisition (MP-Dixon-GRASP) for fat/water-separated (water-specific) T1 mapping. METHODS An adiabatic non-selective 180° inversion-recovery pulse was added to a gradient-echo-based golden-angle stack-of-stars sequence for magnetization-prepared 3D single-echo or 3D multi-echo acquisition. In combination with subspace-based GRASP-Pro reconstruction, the sequence allows for standard T1 mapping (MP-GRASP) or fat/water-separated T1 mapping (MP-Dixon-GRASP), respectively. The accuracy of T1 mapping using MP-GRASP was evaluated in a phantom and volunteers (brain and liver) against clinically accepted reference methods. The repeatability of T1 estimation was also assessed in the phantom and volunteers. The performance of MP-Dixon-GRASP for water-specific T1 mapping was evaluated in a fat/water phantom and volunteers (brain and liver). RESULTS ROI-based mean T1 values are correlated between the references and MP-GRASP in the phantom (R2 = 1.0), brain (R2 = 0.96), and liver (R2 = 0.73). MP-GRASP achieved good repeatability of T1 estimation in the phantom (R2 = 1.0), brain (R2 = 0.99), and liver (R2 = 0.82). Water-specific T1 is different from in-phase and out-of-phase composite T1 (composite T1 when fat and water signal are mixed in phase or out of phase) both in the phantom and volunteers. CONCLUSION This work demonstrated the initial performance of MP-GRASP and MP-Dixon-GRASP MRI for rapid 3D T1 mapping and 3D fat/water-separated T1 mapping in the brain (without motion) and in the liver (during free breathing). With fat/water-separated T1 estimation, MP-Dixon-GRASP could be potentially useful for imaging patients with fatty-liver diseases.
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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, NY, USA
| | - Fang Liu
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Georgios Soultanidis
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Chenyu Liu
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Thomas Benkert
- MR Application Development, Siemens Healthcare GmbH, Erlangen, Germany
| | - Kai Tobias Block
- MR Application Development, Siemens Healthcare GmbH, Erlangen, Germany
- Center for Advanced Imaging Innovation and Research (CAIR), New York University School of Medicine, New York, NY, USA
| | - Zahi A. Fayad
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yang Yang
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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van Riel MHC, Yu Z, Hodono S, Xia D, Chandarana H, Fujimoto K, Cloos MA. Free-breathing abdominal T 1 mapping using an optimized MR fingerprinting sequence. NMR IN BIOMEDICINE 2021; 34:e4531. [PMID: 33902155 PMCID: PMC8218311 DOI: 10.1002/nbm.4531] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 04/03/2021] [Accepted: 04/05/2021] [Indexed: 05/31/2023]
Abstract
In this work, we propose a free-breathing magnetic resonance fingerprinting (MRF) method that can be used to obtain B1+ -robust quantitative T1 maps of the abdomen in a clinically acceptable time. A three-dimensional MRF sequence with a radial stack-of-stars trajectory was implemented, and its k-space acquisition ordering was adjusted to improve motion-robustness in the context of MRF. The flip angle pattern was optimized using the Cramér-Rao Lower Bound, and the encoding efficiency of sequences with 300, 600, 900 and 1800 flip angles was evaluated. To validate the sequence, a movable multicompartment phantom was developed. Reference multiparametric maps were acquired under stationary conditions using a previously validated MRF method. Periodic motion of the phantom was used to investigate the motion-robustness of the proposed sequence. The best performing sequence length (600 flip angles) was used to image the abdomen during a free-breathing volunteer scan. When using a series of 600 or more flip angles, the estimated T1 values in the stationary phantom showed good agreement with the reference scan. Phantom experiments revealed that motion-related artifacts can appear in the quantitative maps and confirmed that a motion-robust k-space ordering is essential. The in vivo scan demonstrated that the proposed sequence can produce clean parameter maps while the subject breathes freely. Using this sequence, it is possible to generate B1+ -robust quantitative maps of T1 and B1+ next to M0 -weighted images under free-breathing conditions at a clinically usable resolution within 5 min.
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Affiliation(s)
- Max H. C. van Riel
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Zidan Yu
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, New York, USA
- Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, New York, USA
| | - Shota Hodono
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, New York, USA
- Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, New York, USA
- Centre for Advanced Imaging, University of Queensland, St Lucia, Queensland, Australia
| | - Ding Xia
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Hersh Chandarana
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Koji Fujimoto
- Human Brain Research Center, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Department of Real World Data Research and Development, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Martijn A. Cloos
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, New York, USA
- Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, New York, USA
- Centre for Advanced Imaging, University of Queensland, St Lucia, Queensland, Australia
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MacAskill CJ, Markley M, Farr S, Parsons A, Perino JR, McBennett K, Kutney K, Drumm ML, Pritts N, Griswold MA, Ma D, Dell KM, Flask CA, Chen Y. Rapid B 1-Insensitive MR Fingerprinting for Quantitative Kidney Imaging. Radiology 2021; 300:380-387. [PMID: 34100680 DOI: 10.1148/radiol.2021202302] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Background MR fingerprinting (MRF) provides rapid and simultaneous quantification of multiple tissue parameters in a single scan. Purpose To evaluate a rapid kidney MRF technique at 3.0 T in phantoms, healthy volunteers, and patients. Materials and Methods A 15-second kidney MRF acquisition was designed with 12 acquisition segments, a range of low flip angles (5°-12°), multiple magnetization preparation schema (T1, T2, and fat suppression), and an undersampled spiral trajectory. This technique was first validated in vitro using standardized T1 and T2 phantoms. Kidney T1 and T2 maps were then obtained for 10 healthy adult volunteers (mean age ± standard deviation, 35 years ± 13; six men) and three pediatric patients with autosomal recessive polycystic kidney disease (ARPKD) (mean age, 10 years ± 3; two boys) between August 2019 and October 2020 to evaluate the method in vivo. Results Results in nine phantoms showed good agreement with spin-echo-based T1 and T2 values (R2 > 0.99). In vivo MRF kidney T1 and T2 assessments in healthy adult volunteers (cortex: T1, 1362 msec ± 5; T2, 64 msec ± 5; medulla: T1, 1827 msec ± 94; T2, 69 msec ± 3) were consistent with values in the literature but with improved precision in comparison with prior MRF implementations. In vivo MRF-based kidney T1 and T2 values with and without B1 correction were in good agreement (R2 > 0.96, P < .001), demonstrating limited sensitivity to B1 field inhomogeneities. Additional MRF reconstructions using the first nine segments of the MRF profiles (11-second acquisition time) were in good agreement with the reconstructions using 12 segments (15-second acquisition time) (R2 > 0.87, P < .001). Repeat kidney MRF scans for the three patients with ARPKD on successive days also demonstrated good reproducibility (T1 and T2: <3% difference). Conclusion A kidney MR fingerprinting method provided in vivo kidney T1 and T2 maps at 3.0 T in a single breath hold with improved precision and no need for B1 correction. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Laustsen in this issue.
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Affiliation(s)
- Christina J MacAskill
- From the Departments of Radiology (C.J.M., S.F., J.R.P., N.P., M.A.G., D.M., C.A.F., Y.C.), Genetics and Genome Sciences (M.L.D.), Pediatrics (M.L.D., K.M.D., C.A.F.), and Biomedical Engineering (M.A.G., D.M., C.A.F.), Case Western Reserve University, 11100 Euclid Ave, Bowell Building, Room B131, Cleveland, OH 44106; Departments of Radiology (M.M.) and Pediatrics (K.M., K.K.), University Hospitals Cleveland Medical Center, Cleveland, Ohio; and Center for Pediatric Nephrology, Cleveland Clinic Children's Hospital, Cleveland, Ohio (A.P., K.M.D.)
| | - Michael Markley
- From the Departments of Radiology (C.J.M., S.F., J.R.P., N.P., M.A.G., D.M., C.A.F., Y.C.), Genetics and Genome Sciences (M.L.D.), Pediatrics (M.L.D., K.M.D., C.A.F.), and Biomedical Engineering (M.A.G., D.M., C.A.F.), Case Western Reserve University, 11100 Euclid Ave, Bowell Building, Room B131, Cleveland, OH 44106; Departments of Radiology (M.M.) and Pediatrics (K.M., K.K.), University Hospitals Cleveland Medical Center, Cleveland, Ohio; and Center for Pediatric Nephrology, Cleveland Clinic Children's Hospital, Cleveland, Ohio (A.P., K.M.D.)
| | - Susan Farr
- From the Departments of Radiology (C.J.M., S.F., J.R.P., N.P., M.A.G., D.M., C.A.F., Y.C.), Genetics and Genome Sciences (M.L.D.), Pediatrics (M.L.D., K.M.D., C.A.F.), and Biomedical Engineering (M.A.G., D.M., C.A.F.), Case Western Reserve University, 11100 Euclid Ave, Bowell Building, Room B131, Cleveland, OH 44106; Departments of Radiology (M.M.) and Pediatrics (K.M., K.K.), University Hospitals Cleveland Medical Center, Cleveland, Ohio; and Center for Pediatric Nephrology, Cleveland Clinic Children's Hospital, Cleveland, Ohio (A.P., K.M.D.)
| | - Ashlee Parsons
- From the Departments of Radiology (C.J.M., S.F., J.R.P., N.P., M.A.G., D.M., C.A.F., Y.C.), Genetics and Genome Sciences (M.L.D.), Pediatrics (M.L.D., K.M.D., C.A.F.), and Biomedical Engineering (M.A.G., D.M., C.A.F.), Case Western Reserve University, 11100 Euclid Ave, Bowell Building, Room B131, Cleveland, OH 44106; Departments of Radiology (M.M.) and Pediatrics (K.M., K.K.), University Hospitals Cleveland Medical Center, Cleveland, Ohio; and Center for Pediatric Nephrology, Cleveland Clinic Children's Hospital, Cleveland, Ohio (A.P., K.M.D.)
| | - Jacob R Perino
- From the Departments of Radiology (C.J.M., S.F., J.R.P., N.P., M.A.G., D.M., C.A.F., Y.C.), Genetics and Genome Sciences (M.L.D.), Pediatrics (M.L.D., K.M.D., C.A.F.), and Biomedical Engineering (M.A.G., D.M., C.A.F.), Case Western Reserve University, 11100 Euclid Ave, Bowell Building, Room B131, Cleveland, OH 44106; Departments of Radiology (M.M.) and Pediatrics (K.M., K.K.), University Hospitals Cleveland Medical Center, Cleveland, Ohio; and Center for Pediatric Nephrology, Cleveland Clinic Children's Hospital, Cleveland, Ohio (A.P., K.M.D.)
| | - Kimberly McBennett
- From the Departments of Radiology (C.J.M., S.F., J.R.P., N.P., M.A.G., D.M., C.A.F., Y.C.), Genetics and Genome Sciences (M.L.D.), Pediatrics (M.L.D., K.M.D., C.A.F.), and Biomedical Engineering (M.A.G., D.M., C.A.F.), Case Western Reserve University, 11100 Euclid Ave, Bowell Building, Room B131, Cleveland, OH 44106; Departments of Radiology (M.M.) and Pediatrics (K.M., K.K.), University Hospitals Cleveland Medical Center, Cleveland, Ohio; and Center for Pediatric Nephrology, Cleveland Clinic Children's Hospital, Cleveland, Ohio (A.P., K.M.D.)
| | - Katherine Kutney
- From the Departments of Radiology (C.J.M., S.F., J.R.P., N.P., M.A.G., D.M., C.A.F., Y.C.), Genetics and Genome Sciences (M.L.D.), Pediatrics (M.L.D., K.M.D., C.A.F.), and Biomedical Engineering (M.A.G., D.M., C.A.F.), Case Western Reserve University, 11100 Euclid Ave, Bowell Building, Room B131, Cleveland, OH 44106; Departments of Radiology (M.M.) and Pediatrics (K.M., K.K.), University Hospitals Cleveland Medical Center, Cleveland, Ohio; and Center for Pediatric Nephrology, Cleveland Clinic Children's Hospital, Cleveland, Ohio (A.P., K.M.D.)
| | - Mitchell L Drumm
- From the Departments of Radiology (C.J.M., S.F., J.R.P., N.P., M.A.G., D.M., C.A.F., Y.C.), Genetics and Genome Sciences (M.L.D.), Pediatrics (M.L.D., K.M.D., C.A.F.), and Biomedical Engineering (M.A.G., D.M., C.A.F.), Case Western Reserve University, 11100 Euclid Ave, Bowell Building, Room B131, Cleveland, OH 44106; Departments of Radiology (M.M.) and Pediatrics (K.M., K.K.), University Hospitals Cleveland Medical Center, Cleveland, Ohio; and Center for Pediatric Nephrology, Cleveland Clinic Children's Hospital, Cleveland, Ohio (A.P., K.M.D.)
| | - Nicole Pritts
- From the Departments of Radiology (C.J.M., S.F., J.R.P., N.P., M.A.G., D.M., C.A.F., Y.C.), Genetics and Genome Sciences (M.L.D.), Pediatrics (M.L.D., K.M.D., C.A.F.), and Biomedical Engineering (M.A.G., D.M., C.A.F.), Case Western Reserve University, 11100 Euclid Ave, Bowell Building, Room B131, Cleveland, OH 44106; Departments of Radiology (M.M.) and Pediatrics (K.M., K.K.), University Hospitals Cleveland Medical Center, Cleveland, Ohio; and Center for Pediatric Nephrology, Cleveland Clinic Children's Hospital, Cleveland, Ohio (A.P., K.M.D.)
| | - Mark A Griswold
- From the Departments of Radiology (C.J.M., S.F., J.R.P., N.P., M.A.G., D.M., C.A.F., Y.C.), Genetics and Genome Sciences (M.L.D.), Pediatrics (M.L.D., K.M.D., C.A.F.), and Biomedical Engineering (M.A.G., D.M., C.A.F.), Case Western Reserve University, 11100 Euclid Ave, Bowell Building, Room B131, Cleveland, OH 44106; Departments of Radiology (M.M.) and Pediatrics (K.M., K.K.), University Hospitals Cleveland Medical Center, Cleveland, Ohio; and Center for Pediatric Nephrology, Cleveland Clinic Children's Hospital, Cleveland, Ohio (A.P., K.M.D.)
| | - Dan Ma
- From the Departments of Radiology (C.J.M., S.F., J.R.P., N.P., M.A.G., D.M., C.A.F., Y.C.), Genetics and Genome Sciences (M.L.D.), Pediatrics (M.L.D., K.M.D., C.A.F.), and Biomedical Engineering (M.A.G., D.M., C.A.F.), Case Western Reserve University, 11100 Euclid Ave, Bowell Building, Room B131, Cleveland, OH 44106; Departments of Radiology (M.M.) and Pediatrics (K.M., K.K.), University Hospitals Cleveland Medical Center, Cleveland, Ohio; and Center for Pediatric Nephrology, Cleveland Clinic Children's Hospital, Cleveland, Ohio (A.P., K.M.D.)
| | - Katherine M Dell
- From the Departments of Radiology (C.J.M., S.F., J.R.P., N.P., M.A.G., D.M., C.A.F., Y.C.), Genetics and Genome Sciences (M.L.D.), Pediatrics (M.L.D., K.M.D., C.A.F.), and Biomedical Engineering (M.A.G., D.M., C.A.F.), Case Western Reserve University, 11100 Euclid Ave, Bowell Building, Room B131, Cleveland, OH 44106; Departments of Radiology (M.M.) and Pediatrics (K.M., K.K.), University Hospitals Cleveland Medical Center, Cleveland, Ohio; and Center for Pediatric Nephrology, Cleveland Clinic Children's Hospital, Cleveland, Ohio (A.P., K.M.D.)
| | - Chris A Flask
- From the Departments of Radiology (C.J.M., S.F., J.R.P., N.P., M.A.G., D.M., C.A.F., Y.C.), Genetics and Genome Sciences (M.L.D.), Pediatrics (M.L.D., K.M.D., C.A.F.), and Biomedical Engineering (M.A.G., D.M., C.A.F.), Case Western Reserve University, 11100 Euclid Ave, Bowell Building, Room B131, Cleveland, OH 44106; Departments of Radiology (M.M.) and Pediatrics (K.M., K.K.), University Hospitals Cleveland Medical Center, Cleveland, Ohio; and Center for Pediatric Nephrology, Cleveland Clinic Children's Hospital, Cleveland, Ohio (A.P., K.M.D.)
| | - Yong Chen
- From the Departments of Radiology (C.J.M., S.F., J.R.P., N.P., M.A.G., D.M., C.A.F., Y.C.), Genetics and Genome Sciences (M.L.D.), Pediatrics (M.L.D., K.M.D., C.A.F.), and Biomedical Engineering (M.A.G., D.M., C.A.F.), Case Western Reserve University, 11100 Euclid Ave, Bowell Building, Room B131, Cleveland, OH 44106; Departments of Radiology (M.M.) and Pediatrics (K.M., K.K.), University Hospitals Cleveland Medical Center, Cleveland, Ohio; and Center for Pediatric Nephrology, Cleveland Clinic Children's Hospital, Cleveland, Ohio (A.P., K.M.D.)
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Khajehim M, Christen T, Tam F, Graham SJ. Streamlined magnetic resonance fingerprinting: Fast whole-brain coverage with deep-learning based parameter estimation. Neuroimage 2021; 238:118237. [PMID: 34091035 DOI: 10.1016/j.neuroimage.2021.118237] [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: 02/17/2021] [Revised: 05/26/2021] [Accepted: 06/02/2021] [Indexed: 01/02/2023] Open
Abstract
Magnetic resonance fingerprinting (MRF) is a quantitative MRI (qMRI) framework that provides simultaneous estimates of multiple relaxation parameters as well as metrics of field inhomogeneity in a single acquisition. However, current challenges exist in the forms of (1) scan time; (2) need for custom image reconstruction; (3) large dictionary sizes; (4) long dictionary-matching time. This study aims to introduce a novel streamlined magnetic-resonance fingerprinting (sMRF) framework based on a single-shot echo-planar imaging (EPI) sequence to simultaneously estimate tissue T1, T2, and T2* with integrated B1+ correction. Encouraged by recent work on EPI-based MRF, we developed a method that combines spin-echo EPI with gradient-echo EPI to achieve T2 in addition to T1 and T2* quantification. To this design, we add simultaneous multi-slice (SMS) acceleration to enable full-brain coverage in a few minutes. Moreover, in the parameter-estimation step, we use deep learning to train a deep neural network (DNN) to accelerate the estimation process by orders of magnitude. Notably, due to the high image quality of the EPI scans, the training process can rely simply on Bloch-simulated data. The DNN also removes the need for storing large dictionaries. Phantom scans along with in-vivo multi-slice scans from seven healthy volunteers were acquired with resolutions of 1.1×1.1×3 mm3 and 1.7×1.7×3 mm3, and the results were validated against ground truth measurements. Excellent correspondence was found between our T1, T2, and T2* estimates and results obtained from standard approaches. In the phantom scan, a strong linear relationship (R = 1-1.04, R2>0.96) was found for all parameter estimates, with a particularly high agreement for T2 estimation (R2>0.99). Similar findings are reported for the in-vivo human data for all of our parameter estimates. Incorporation of DNN results in a reduction of parameter estimation time on the order of 1000 x and a reduction in storage requirements on the order of 2500 x while achieving highly similar results as conventional dictionary matching (%differences of 7.4 ± 0.4%, 3.6 ± 0.3% and 6.0 ± 0.4% error in T1, T2, and T2* estimation). Thus, sMRF has the potential to be the method of choice for future MRF studies by providing ease of implementation, fast whole-brain coverage, and ultra-fast T1/T2/T2* estimation.
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Affiliation(s)
- Mahdi Khajehim
- Department of Medical Biophysics, University of Toronto, 101 College St Suite 15-701, Toronto, ON M5G 1L7, Canada.
| | - Thomas Christen
- Grenoble Institute of Neuroscience, Inserm, Grenoble, France
| | - Fred Tam
- Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Simon J Graham
- Department of Medical Biophysics, University of Toronto, 101 College St Suite 15-701, Toronto, ON M5G 1L7, Canada; Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, ON, Canada
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50
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Tippareddy C, Zhao W, Sunshine JL, Griswold M, Ma D, Badve C. Magnetic resonance fingerprinting: an overview. Eur J Nucl Med Mol Imaging 2021; 48:4189-4200. [PMID: 34037831 DOI: 10.1007/s00259-021-05384-2] [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: 12/21/2020] [Accepted: 04/25/2021] [Indexed: 12/17/2022]
Abstract
Magnetic resonance fingerprinting (MRF) is an evolving quantitative MRI framework consisting of unique data acquisition, processing, visualization, and interpretation steps. MRF is capable of simultaneously producing multiple high-resolution property maps including T1, T2, M0, ADC, and T2* measurements. While a relatively new technology, MRF has undergone rapid development for a variety of clinical applications from brain tumor characterization and epilepsy imaging to characterization of prostate cancer, cardiac imaging, among others. This paper will provide a brief overview of current state of MRF technology including highlights of technical and clinical advances. We will conclude with a brief discussion of the challenges that need to be overcome to establish MRF as a quantitative imaging biomarker.
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Affiliation(s)
- Charit Tippareddy
- Case Western Reserve University School of Medicine, 11100 Euclid Ave., Cleveland, OH, 44106, USA
| | - Walter Zhao
- Case Western Reserve University School of Medicine, 11100 Euclid Ave., Cleveland, OH, 44106, USA
| | - Jeffrey L Sunshine
- Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, 11100 Euclid Ave., Cleveland, OH, 44106, USA
| | - Mark Griswold
- Department of Biomedical Engineering, Case Western Reserve University, 11100 Euclid Ave., Cleveland, OH, 44106, USA.,Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, 11100 Euclid Ave., Cleveland, OH, 44106, USA
| | - Dan Ma
- Department of Biomedical Engineering, Case Western Reserve University, 11100 Euclid Ave., Cleveland, OH, 44106, USA
| | - Chaitra Badve
- Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, 11100 Euclid Ave., Cleveland, OH, 44106, USA.
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