1.School of Biomedical Engineering, Guangdong Provincial Orthopedic Research Institute, Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China
2.Guangdong Provincial Key Laboratory of Medical Imaging Processing, Southern Medical University, Guangzhou 510515, China, Guangdong Provincial Orthopedic Research Institute, Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China
3.Department of Imaging, Guangdong Provincial Orthopedic Research Institute, Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China
Objective To propose a single repetition time (TR) quantitative magnetic susceptibility imaging method for the lumbar spine using bipolar readout gradient, and compare the quantitative magnetic susceptibility measurement using single TR and dual TR methods for the lumbar spine with different bone densities. Methods A translation correction method was proposed to correct spatial misalignment along the frequency encoding direction between positive and negative gradient readout images, and the phase difference between the images was eliminated using a phase correction method. The data of lumbar vertebrae L1-L5 were collected using single TR and dual TR methods from 6 normal individuals, 2 patients with osteopenia, and 2 patients with osteoporosis. The magnetic susceptibility map was reconstructed, the quantitative results of single TR before and after correction were compared with those of the dual TR method. Results The linear regression result of the lumbar spine magnetic susceptibility values obtained by the single TR method before calibration and the dual TR method is Y=0.64*X-11.61. The linear regression result of the lumbar spine magnetic susceptibility values corrected by the single TR method and the dual TR method is Y=1.03*X+0.25. The results of the corrected single TR method were highly consistent with those of the dual TR method, and the calibrated single TR method could effectively distinguish osteopenia and osteoporosis patients from normal individuals. Conclusion The calibrated single TR bipolar readout gradient method can generate artifact-free lumbar spine quantitative magnetic susceptibility distribution maps and reduce data acquisition time by 50%.
本实验在Philips 3T磁共振(Achieva Best, The Netherlands)上进行,对6名正常人,2名骨质减少患者以及2名骨质疏松患者进行了MRI扫描,磁共振的接收线圈采用12通道的体线圈。所有受试者签署了知情者同意书,本研究经南方医科大学第三附属医院伦理委员会批准(伦理批号:2023-伦审-008)。
WangY, LiuT. Quantitative susceptibility mapping (QSM): decoding MRI data for a tissue magnetic biomarker[J]. Magn Reson Med, 2015, 73(1): 82-101. doi:10.1002/mrm.25358
[2]
WisnieffC, RamananS, OlesikJ, et al. Quantitative susceptibility mapping (QSM) of white matter multiple sclerosis lesions: Interpreting positive susceptibility and the presence of iron[J]. Magn Reson Med, 2015, 74(2): 564-70. doi:10.1002/mrm.25420
[3]
RavanfarP, LoiSM, SyedaWT, et al. Systematic review: quantitative susceptibility mapping (QSM) of brain iron profile in neurodegenerative diseases[J]. Front Neurosci, 2021, 15: 618435. doi:10.3389/fnins.2021.618435
[4]
YangJ, LvM, HanL, et al. Evaluation of brain iron deposition in different cerebral arteries of acute ischaemic stroke patients using quantitative susceptibility mapping[J]. Clin Radiol, 2024, 79(4): e592-8. doi:10.1016/j.crad.2024.01.007
[5]
GuoW, ZhangD, SunJ, et al. Quantitative susceptibility mapping of subcortical iron deposition in Parkinson disease and multiple system atrophy: clinical correlations and diagnostic implications[J]. Quant Imaging Med Surg, 2024, 14(7): 4464-74. doi:10.21037/qims-24-168
[6]
CarpenterKLH, LiW, WeiHJ, et al. Magnetic susceptibility of brain iron is associated with childhood spatial IQ[J]. NeuroImage, 2016, 132: 167-74. doi:10.1016/j.neuroimage.2016.02.028
[7]
LiX, AllenRP, EarleyCJ, et al. Brain iron deficiency in idiopathic restless legs syndrome measured by quantitative magnetic susceptibility at 7 tesla[J]. Sleep Med, 2016, 22: 75-82. doi:10.1016/j.sleep.2016.05.001
[8]
KataikeVM, DesmondPM, StewardC, et al. Iron changes within infarct tissue in ischemic stroke patients after successful reperfusion quantified using QSM[J]. Neuroradiology, 2024, 66(12): 2233-42. doi:10.1007/s00234-024-03444-6
[9]
SharmaSD, HernandoD, HorngDE, et al. Quantitative susceptibility mapping in the abdomen as an imaging biomarker of hepatic iron overload[J]. Magn Reson Med, 2015, 74(3): 673-83. doi:10.1002/mrm.25448
[10]
LinHM, WeiHJ, HeNY, et al. Quantitative susceptibility mapping in combination with water-fat separation for simultaneous liver iron and fat fraction quantification[J]. Eur Radiol, 2018, 28(8): 3494-504. doi:10.1007/s00330-017-5263-4
BueloCJ, VelikinaJ, MaoL, et al. Multicenter, multivendor validation of liver quantitative susceptibility mapping in patients with iron overload at 1.5 T and 3 T[J]. Magn Reson Med, 2025, 93(1): 330-40. doi:10.1002/mrm.30251
[13]
JungM, BressonX, ChanTF, et al. Nonlocal Mumford-Shah regularizers for color image restoration[J]. IEEE Trans Image Process, 2011, 20(6): 1583-98. doi:10.1109/tip.2010.2092433
[14]
LebenatusA, KusterJ, StraubS, et al. In-vitro detection of intramammary-like macrocalcifications using susceptibility-weighted MR imaging techniques at 1.5T[J]. Magn Reson Med Sci, 2024: mp. 2024-75. doi:10.2463/mrms.mp.2024-0075
[15]
BoehmC, SollmannN, MeinekeJ, et al. Preconditioned water-fat total field inversion: Application to spine quantitative susceptibility mapping[J]. Magn Reson Med, 2022, 87(1): 417-30. doi:10.1002/mrm.28903
[16]
DiefenbachMN, MeinekeJ, RuschkeS, et al. On the sensitivity of quantitative susceptibility mapping for measuring trabecular bone density[J]. Magn Reson Med, 2019, 81(3): 1739-54. doi:10.1002/mrm.27531
[17]
GuoY, ChenY, ZhangX, et al. Magnetic susceptibility and fat content in the lumbar spine of postmenopausal women with varying bone mineral density[J]. J Magn Reson Imaging, 2019, 49(4): 1020-8. doi:10.1002/jmri.26279
[18]
GuoY, LiuZ, WenY, et al. Quantitative susceptibility mapping of the spine using in-phase echoes to initialize inhomogeneous field and R2* for the nonconvex optimization problem of fat-water separation[J]. NMR Biomed, 2019, 32(11): e4156. doi:10.1002/nbm.4156
[19]
JerbanS, MaY, JangH, et al. MRI-based bone biomarkers; proceedings of the Seminars in musculoskeletal. Radiology, F, 2024 [C]. doi:10.1055/s-0040-1710355
[20]
LiN, LiXM, XuL, et al. Comparison of QCT and DXA: osteoporosis detection rates in postmenopausal women[J]. Int J Endocrinol, 2013, 2013: 895474. doi:10.1155/2013/895474
[21]
WángYXJ, YuW, LeungJCS, et al. More evidence to support a lower quantitative computed tomography (QCT) lumbar spine bone mineral density (BMD) cutpoint value for classifying osteoporosis among older East Asian women than for Caucasians[J]. Quant Imaging Med Surg, 2024, 14(5): 3239-47. doi:10.21037/qims-24-429
[22]
LiuC, LiW, TongKA, et al. Susceptibility-weighted imaging and quantitative susceptibility mapping in the brain[J]. J Magn Reson Imaging, 2015, 42(1): 23-41. doi:10.1002/jmri.24768
[23]
HopkinsJA, WehrliFW. Magnetic susceptibility measurement of insoluble solids by NMR: magnetic susceptibility of bone[J]. Magn Reson Med, 1997, 37(4): 494-500. doi:10.1002/mrm.1910370404
[24]
BerikolG, EkşiMŞ, AydınL, et al. Subcutaneous fat index: a reliable tool for lumbar spine studies[J]. Eur Radiol, 2022, 32(9): 6504-13. doi:10.1007/s00330-022-08775-7
[25]
BoehmC, SchlaegerS, MeinekeJ, et al. On the water–fat in-phase assumption for quantitative susceptibility mapping[J]. Magn Reson Med, 2023, 89(3): 1068-82. doi:10.1002/mrm.29516
[26]
RuschkeS, EggersH, KooijmanH, et al. Correction of phase errors in quantitative water-fat imaging using a monopolar time-interleaved multi-echo gradient echo sequence[J]. Magn Reson Med, 2017, 78(3): 984-96. doi:10.1002/mrm.26485
YuH, ShimakawaA, McKenzieCA, et al. Multiecho water-fat separation and simultaneous R2* estimation with multifrequency fat spectrum modeling[J]. Magn Reson Med, 2008, 60(5): 1122-34. doi:10.1002/mrm.21737
[29]
ZhouD, LiuT, SpincemailleP, et al. Background field removal by solving the Laplacian boundary value problem[J]. NMR Biomed, 2014, 27(3): 312-9. doi:10.1002/nbm.3064
[30]
LiuJ, LiuT, de RochefortL, et al. Morphology enabled dipole inversion for quantitative susceptibility mapping using structural consistency between the magnitude image and the susceptibility map[J]. NeuroImage, 2012, 59(3): 2560-8. doi:10.1016/j.neuroimage.2011.08.082
[31]
FengW, NeelavalliJ, HaackeEM. Catalytic multiecho phase unwrapping scheme (CAMPUS) in multiecho gradient echo imaging: removing phase wraps on a voxel-by-voxel basis[J]. Magn Reson Med, 2013, 70(1): 117-26. doi:10.1002/mrm.24457
[32]
PetersonP, MånssonS. Fat quantification using multiecho sequences with bipolar gradients: investigation of accuracy and noise performance[J]. Magn Reson Med, 2014, 71(1): 219-29. doi:10.1002/mrm.24657
[33]
YuH, ShimakawaA, McKenzieCA, et al. Phase and amplitude correction for multi-echo water-fat separation with bipolar acquisitions[J]. J Magn Reson Imaging, 2010, 31(5): 1264-71. doi:10.1002/jmri.22111
[34]
QSM Consensus Organization Committee, BilgicB, CostagliM, et al. Recommended implementation of quantitative susceptibility mapping for clinical research in the brain: a consensus of the ISMRM electro-magnetic tissue properties study group[J]. Magn Reson Med, 2024, 91(5): 1834-62. doi:10.1002/mrm.30006
[35]
JerbanS, MaYJ, ChangEY, et al. A UTE-based biomarker panel in osteoporosis [M]. MRI of Short-and Ultrashort-T2 Tissues: Making the Invisible Visible. Springer. 2024: 427-39. doi:10.1007/978-3-031-35197-6_34
[36]
ShinSH, ChaeHD, SupranaA, et al. UTE MRI technical developments and applications in osteoporosis: a review [J]. Frontiers in Endocrinology, 2025, 16: 1510010. doi:10.3389/fendo.2025.1510010
[37]
StreichenbergerB, SantinM, RocheS, et al. Quantitative susceptibility mapping of the cervical spinal cord for MS monitoring; proceedings of the 2025-20th MRI Workshop: MS lesions imaging: update on chronic active lesion and the central vein sign, F, 2025 [C].