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Value of Quantitative Susceptibility Mapping in Clinical Neuroradiology.

Fushimi, Y ; Nakajima, S ; et al.
In: Journal of magnetic resonance imaging : JMRI, Jg. 59 (2024-06-01), Heft 6, S. 1914-1929
Online academicJournal

Titel:
Value of Quantitative Susceptibility Mapping in Clinical Neuroradiology.
Autor/in / Beteiligte Person: Fushimi, Y ; Nakajima, S ; Sakata, A ; Okuchi, S ; Otani, S ; Nakamoto, Y
Link:
Zeitschrift: Journal of magnetic resonance imaging : JMRI, Jg. 59 (2024-06-01), Heft 6, S. 1914-1929
Veröffentlichung: <2005-> : Hoboken , N.J. : Wiley-Liss ; <i>Original Publication</i>: Chicago, IL : Society for Magnetic Resonance Imaging, c1991-, 2024
Medientyp: academicJournal
ISSN: 1522-2586 (electronic)
DOI: 10.1002/jmri.29010
Schlagwort:
  • Humans
  • Brain diagnostic imaging
  • Image Interpretation, Computer-Assisted methods
  • Reproducibility of Results
  • Brain Diseases diagnostic imaging
  • Neuroimaging methods
  • Image Enhancement methods
  • Brain Mapping methods
  • Algorithms
  • Magnetic Resonance Imaging methods
Sonstiges:
  • Nachgewiesen in: MEDLINE
  • Sprachen: English
  • Publication Type: Journal Article; Review
  • Language: English
  • [J Magn Reson Imaging] 2024 Jun; Vol. 59 (6), pp. 1914-1929. <i>Date of Electronic Publication: </i>2023 Sep 08.
  • MeSH Terms: Magnetic Resonance Imaging* / methods ; Humans ; Brain / diagnostic imaging ; Image Interpretation, Computer-Assisted / methods ; Reproducibility of Results ; Brain Diseases / diagnostic imaging ; Neuroimaging / methods ; Image Enhancement / methods ; Brain Mapping / methods ; Algorithms
  • References: Haacke EM, Xu Y, Cheng YC, Reichenbach JR. Susceptibility weighted imaging (SWI). Magn Reson Med 2004;52(3):612‐618. ; Deistung A, Schweser F, Reichenbach JR. Overview of quantitative susceptibility mapping. NMR Biomed 2017;30(4):e3569. ; de Rochefort L, Brown R, Prince MR, Wang Y. Quantitative MR susceptibility mapping using piece‐wise constant regularized inversion of the magnetic field. Magn Reson Med 2008;60(4):1003‐1009. ; Wharton S, Schafer A, Bowtell R. Susceptibility mapping in the human brain using threshold‐based k‐space division. Magn Reson Med 2010;63(5):1292‐1304. ; Li W, Wu B, Liu C. Quantitative susceptibility mapping of human brain reflects spatial variation in tissue composition. Neuroimage 2011;55(4):1645‐1656. ; Haacke EM, Liu S, Buch S, Zheng W, Wu D, Ye Y. Quantitative susceptibility mapping: Current status and future directions. Magn Reson Imaging 2015;33(1):1‐25. ; Wang Y, Spincemaille P, Liu Z, et al. Clinical quantitative susceptibility mapping (QSM): Biometal imaging and its emerging roles in patient care. J Magn Reson Imaging 2017;46(4):951‐971. ; Vinayagamani S, Sheelakumari R, Sabarish S, et al. Quantitative susceptibility mapping: Technical considerations and clinical applications in neuroimaging. J Magn Reson Imaging 2021;53(1):23‐37. ; Liu C, Li W, Tong KA, Yeom KW, Kuzminski S. Susceptibility‐weighted imaging and quantitative susceptibility mapping in the brain. J Magn Reson Imaging 2015;42(1):23‐41. ; Smith SM. Fast robust automated brain extraction. Hum Brain Mapp 2002;17(3):143‐155. ; Abdul‐Rahman HS, Gdeisat MA, Burton DR, Lalor MJ, Lilley F, Moore CJ. Fast and robust three‐dimensional best path phase unwrapping algorithm. Appl Optics 2007;46(26):6623‐6635. ; Jenkinson M. Fast, automated, N‐dimensional phase‐unwrapping algorithm. Magn Reson Med 2003;49(1):193‐197. ; Schweser F, Deistung A, Lehr BW, Reichenbach JR. Quantitative imaging of intrinsic magnetic tissue properties using MRI signal phase: An approach to in vivo brain iron metabolism? Neuroimage 2011;54(4):2789‐2807. ; Sun H, Wilman AH. Background field removal using spherical mean value filtering and Tikhonov regularization. Magn Reson Med 2014;71(3):1151‐1157. ; Li W, Avram AV, Wu B, Xiao X, Liu C. Integrated Laplacian‐based phase unwrapping and background phase removal for quantitative susceptibility mapping. NMR Biomed 2014;27(2):219‐227. ; de Rochefort L, Liu T, Kressler B, et al. Quantitative susceptibility map reconstruction from MR phase data using bayesian regularization: Validation and application to brain imaging. Magn Reson Med 2010;63(1):194‐206. ; Schweser F, Deistung A, Lehr BW, Reichenbach JR. Differentiation between diamagnetic and paramagnetic cerebral lesions based on magnetic susceptibility mapping. Med Phys 2010;37(10):5165‐5178. ; Liu J, Liu T, de Rochefort L, et al. Morphology enabled dipole inversion for quantitative susceptibility mapping using structural consistency between the magnitude image and the susceptibility map. Neuroimage 2012;59(3):2560‐2568. ; Langkammer C, Schweser F, Shmueli K, et al. Quantitative susceptibility mapping: Report from the 2016 reconstruction challenge. Magn Reson Med 2018;79(3):1661‐1673. ; Jung W, Bollmann S, Lee J. Overview of quantitative susceptibility mapping using deep learning: Current status, challenges and opportunities. NMR Biomed 2022;35(4):e4292. ; Duyn JH, van Gelderen P, Li TQ, de Zwart JA, Koretsky AP, Fukunaga M. High‐field MRI of brain cortical substructure based on signal phase. Proc Natl Acad Sci U S A 2007;104(28):11796‐11801. ; Bilgic B, Xie L, Dibb R, et al. Rapid multi‐orientation quantitative susceptibility mapping. Neuroimage 2016;125:1131‐1141. ; Langkammer C, Bredies K, Poser BA, et al. Fast quantitative susceptibility mapping using 3D EPI and total generalized variation. Neuroimage 2015;111:622‐630. ; Wicaksono KP, Fushimi Y, Nakajima S, et al. Two‐minute quantitative susceptibility mapping from three‐dimensional Echo‐planar imaging: Accuracy, reliability, and detection performance in patients with cerebral microbleeds. Invest Radiol 2021;56(2):69‐77. ; Haacke EM, Chen Y, Utriainen D, et al. STrategically acquired gradient Echo (STAGE) imaging, part III: Technical advances and clinical applications of a rapid multi‐contrast multi‐parametric brain imaging method. Magn Reson Imaging 2020;65:15‐26. ; Lancione M, Donatelli G, Cecchi P, Cosottini M, Tosetti M, Costagli M. Echo‐time dependency of quantitative susceptibility mapping reproducibility at different magnetic field strengths. Neuroimage 2019;197:557‐564. ; Liu C. Susceptibility tensor imaging. Magn Reson Med 2010;63(6):1471‐1477. ; Hinoda T, Fushimi Y, Okada T, et al. Quantitative susceptibility mapping at 3 T and 1.5 T: Evaluation of consistency and reproducibility. Invest Radiol 2015;50(8):522‐530. ; Deh K, Nguyen TD, Eskreis‐Winkler S, et al. Reproducibility of quantitative susceptibility mapping in the brain at two field strengths from two vendors. J Magn Reson Imaging 2015;42(6):1592‐1600. ; Okada T, Fujimoto K, Fushimi Y, et al. Neuroimaging at 7 tesla: A pictorial narrative review. Quant Imaging Med Surg 2022;12(6):3406‐3435. ; Spincemaille P, Anderson J, Wu G, et al. Quantitative susceptibility mapping: MRI at 7T versus 3T. J Neuroimaging 2020;30(1):65‐75. ; Schweser F, Deistung A, Lehr BW, Sommer K, Reichenbach JR. SEMI‐TWInS: Simultaneous extraction of myelin and iron using a T2*‐weighted imaging sequence. Proc Intl Soc Mag Reson Med 2011;19:120. ; Shin HG, Lee J, Yun YH, et al. Chi‐separation: Magnetic susceptibility source separation toward iron and myelin mapping in the brain. Neuroimage 2021;240:118371. ; Chen J, Gong NJ, Chaim KT, Otaduy MCG, Liu C. Decompose quantitative susceptibility mapping (QSM) to sub‐voxel diamagnetic and paramagnetic components based on gradient‐echo MRI data. Neuroimage 2021;242:118477. ; Dimov AV, Gupta A, Kopell BH, Wang Y. High‐resolution QSM for functional and structural depiction of subthalamic nuclei in DBS presurgical mapping. J Neurosurg 2018;131(2):360‐367. ; Schenck JF. The role of magnetic susceptibility in magnetic resonance imaging: MRI magnetic compatibility of the first and second kinds. Med Phys 1996;23(6):815‐850. ; Todorich B, Pasquini JM, Garcia CI, Paez PM, Connor JR. Oligodendrocytes and myelination: The role of iron. Glia 2009;57(5):467‐478. ; Harada T, Kudo K, Fujima N, et al. Quantitative susceptibility mapping: Basic methods and clinical applications. Radiographics 2022;42(4):1161‐1176. ; Li W, Wu B, Batrachenko A, et al. Differential developmental trajectories of magnetic susceptibility in human brain gray and white matter over the lifespan. Hum Brain Mapp 2014;35(6):2698‐2713. ; Persson N, Wu J, Zhang Q, et al. Age and sex related differences in subcortical brain iron concentrations among healthy adults. Neuroimage 2015;122:385‐398. ; Zhang Y, Shi J, Wei H, Han V, Zhu WZ, Liu C. Neonate and infant brain development from birth to 2 years assessed using MRI‐based quantitative susceptibility mapping. Neuroimage 2019;185:349‐360. ; Otani S, Fushimi Y, Iwanaga K, et al. Evaluation of deep gray matter for early brain development using quantitative susceptibility mapping. Eur Radiol 2023;33(6):4488‐4499. ; Kim H, Jang J, Kang J, et al. Clinical implications of focal mineral deposition in the Globus pallidus on CT and quantitative susceptibility mapping of MRI. Korean J Radiol 2022;23(7):742‐751. ; Oshima S, Fushimi Y, Okada T, et al. Brain MRI with quantitative susceptibility mapping: Relationship to CT attenuation values. Radiology 2020;294(3):600‐609. ; Fazekas F, Kleinert R, Roob G, et al. Histopathologic analysis of foci of signal loss on gradient‐echo T2*‐weighted MR images in patients with spontaneous intracerebral hemorrhage: Evidence of microangiopathy‐related microbleeds. AJNR Am J Neuroradiol 1999;20(4):637‐642. ; Klohs J, Deistung A, Schweser F, et al. Detection of cerebral microbleeds with quantitative susceptibility mapping in the ArcAbeta mouse model of cerebral amyloidosis. J Cereb Blood Flow Metab 2011;31(12):2282‐2292. ; Chung KK, Anderson NE, Hutchinson D, Synek B, Barber PA. Cerebral amyloid angiopathy related inflammation: Three case reports and a review. J Neurol Neurosurg Psychiatry 2011;82(1):20‐26. ; Kikuta K, Takagi Y, Nozaki K, et al. Early experience with 3‐T magnetic resonance tractography in the surgery of cerebral arteriovenous malformations in and around the visual pathway. Neurosurgery 2006;58(2):331‐337. ; Fujimura M, Tominaga T, Kuroda S, et al. 2021 Japanese guidelines for the Management of Moyamoya Disease: Guidelines from the research committee on Moyamoya disease and Japan stroke society. Neurol Med Chir (Tokyo) 2022;62(4):165‐170. ; Mori N, Miki Y, Kikuta K, et al. Microbleeds in moyamoya disease: Susceptibility‐weighted imaging versus T2*‐weighted imaging at 3 tesla. Invest Radiol 2008;43(8):574‐579. ; Oshima S, Fushimi Y, Okada T, et al. Neuromelanin‐sensitive magnetic resonance imaging using DANTE pulse. Mov Disord 2021;36(4):874‐882. ; Furukawa K, Shima A, Kambe D, et al. Motor progression and nigrostriatal neurodegeneration in Parkinson disease. Ann Neurol 2022;92(1):110‐121. ; He N, Chen Y, LeWitt PA, Yan F, Haacke EM. Application of neuromelanin MR imaging in Parkinson disease. J Magn Reson Imaging 2023;57(2):337‐352. ; Du G, Liu T, Lewis MM, et al. Quantitative susceptibility mapping of the midbrain in Parkinson's disease. Mov Disord 2016;31(3):317‐324. ; Biondetti E, Santin MD, Valabregue R, et al. The spatiotemporal changes in dopamine, neuromelanin and iron characterizing Parkinson's disease. Brain 2021;144(10):3114‐3125. ; Vroegindeweij LHP, Bossoni L, Boon AJW, et al. Quantification of different iron forms in the aceruloplasminemia brain to explore iron‐related neurodegeneration. Neuroimage Clin 2021;30:102657. ; Zeng J, Xing W, Liao W, Wang X. Magnetic resonance imaging, susceptibility weighted imaging and quantitative susceptibility mapping findings of pantothenate kinase‐associated neurodegeneration. J Clin Neurosci 2019;59:20‐28. ; Dusek P, Mekle R, Skowronska M, et al. Brain iron and metabolic abnormalities in C19orf12 mutation carriers: A 7.0 tesla MRI study in mitochondrial membrane protein‐associated neurodegeneration. Mov Disord 2020;35(1):142‐150. ; Bhattarai A, Egan GF, Talman P, Chua P, Chen Z. Magnetic resonance iron imaging in amyotrophic lateral sclerosis. J Magn Reson Imaging 2022;55(5):1283‐1300. ; Schweitzer AD, Liu T, Gupta A, et al. Quantitative susceptibility mapping of the motor cortex in amyotrophic lateral sclerosis and primary lateral sclerosis. AJR Am J Roentgenol 2015;204(5):1086‐1092. ; Conte G, Contarino VE, Casale S, et al. Amyotrophic lateral sclerosis phenotypes significantly differ in terms of magnetic susceptibility properties of the precentral cortex. Eur Radiol 2021;31(7):5272‐5280. ; Acosta‐Cabronero J, Machts J, Schreiber S, et al. Quantitative susceptibility MRI to detect brain iron in amyotrophic lateral sclerosis. Radiology 2018;289(1):195‐203. ; Sugiyama A, Sato N, Kimura Y, et al. Quantifying iron deposition in the cerebellar subtype of multiple system atrophy and spinocerebellar ataxia type 6 by quantitative susceptibility mapping. J Neurol Sci 2019;407:116525. ; Deistung A, Jaschke D, Draganova R, et al. Quantitative susceptibility mapping reveals alterations of dentate nuclei in common types of degenerative cerebellar ataxias. Brain Commun 2022;4(1):fcab306. ; Ferro A, Sheeler C, Rosa JG, Cvetanovic M. Role of microglia in ataxias. J Mol Biol 2019;431(9):1792‐1804. ; Sasaki H, Kojima H, Yabe I, et al. Neuropathological and molecular studies of spinocerebellar ataxia type 6 (SCA6). Acta Neuropathol 1998;95(2):199‐204. ; Tikka S, Baumann M, Siitonen M, et al. CADASIL and CARASIL. Brain Pathol 2014;24(5):525‐544. ; Hong H, Wang S, Yu X, et al. White matter tract injury by MRI in CADASIL patients is associated with iron accumulation. J Magn Reson Imaging 2023;57(1):238‐245. ; van Rooden S, Versluis MJ, Liem MK, et al. Cortical phase changes in Alzheimer's disease at 7T MRI: A novel imaging marker. Alzheimers Dement 2014;10(1):e19‐e26. ; van Bergen JM, Li X, Hua J, et al. Colocalization of cerebral iron with amyloid beta in mild cognitive impairment. Sci Rep 2016;6:35514. ; Yamaguchi A, Kudo K, Sato R, et al. Efficacy of quantitative susceptibility mapping with brain surface correction and vein removal for detecting increase magnetic susceptibility in patients with Alzheimer's disease. Magn Reson Med Sci 2023;22(1):87‐94. ; Ayton S, Faux NG, Bush AI. Alzheimer's disease neuroimaging I. ferritin levels in the cerebrospinal fluid predict Alzheimer's disease outcomes and are regulated by APOE. Nat Commun 2015;6:6760. ; van Rooden S, Doan NT, Versluis MJ, et al. 7T T(2)*‐weighted magnetic resonance imaging reveals cortical phase differences between early‐ and late‐onset Alzheimer's disease. Neurobiol Aging 2015;36(1):20‐26. ; Sato R, Kudo K, Udo N, et al. A diagnostic index based on quantitative susceptibility mapping and voxel‐based morphometry may improve early diagnosis of Alzheimer's disease. Eur Radiol 2022;32(7):4479‐4488. ; Peters MEM, de Brouwer EJM, Bartstra JW, et al. Mechanisms of calcification in Fahr disease and exposure of potential therapeutic targets. Neurol Clin Pract 2020;10(5):449‐457. ; Ukai K, Kosaka K. Diffuse neurofibrillary tangles with calcification (Kosaka‐Shibayama disease) in Japan. Psychiatry Clin Neurosci 2016;70(3):131‐140. ; Hattingen E, Beyle A, Muller A, Klockgether T, Kornblum C. Wernicke encephalopathy: SWI detects petechial hemorrhages in mammillary bodies in vivo. Neurology 2016;87(18):1956‐1957. ; Nakamura Y, Fushimi Y, Hinoda T, et al. Hemosiderin detection inside the mammillary bodies using quantitative susceptibility mapping on patients with Wernicke‐Korsakoff syndrome. Magn Reson Med Sci 2022. https://doi.org/10.2463/mrms.ici.2022-0109. ; Wijdicks EFM. Hepatic encephalopathy. N Engl J Med 2016;375(17):1660‐1670. ; Klos KJ, Ahlskog JE, Kumar N, et al. Brain metal concentrations in chronic liver failure patients with pallidal T1 MRI hyperintensity. Neurology 2006;67(11):1984‐1989. ; Young RJ, Knopp EA. Brain MRI: Tumor evaluation. J Magn Reson Imaging 2006;24(4):709‐724. ; Tanji M, Mineharu Y, Sakata A, et al. High intratumoral susceptibility signal grade on susceptibility‐weighted imaging: A risk factor for hemorrhage after stereotactic biopsy. J Neurosurg 2023;138(1):120‐127. ; Tsukamoto T, Miki Y. Imaging of pituitary tumors: An update with the 5th WHO classifications‐part 2. Neoplasms other than PitNET and tumor‐mimicking lesions. Jpn J Radiol 2023;41:808‐829. ; Kakigi T, Okada T, Kanagaki M, et al. Quantitative imaging values of CT, MR, and FDG‐PET to differentiate pineal parenchymal tumors and germinomas: Are they useful? Neuroradiology 2014;56(4):297‐303. ; Nakasu S, Fukami T, Nakajima M, Watanabe K, Ichikawa M, Matsuda M. Growth pattern changes of meningiomas: Long‐term analysis. Neurosurgery 2005;56(5):946‐955. ; Aiken AH, Akgun H, Tihan T, Barbaro N, Glastonbury C. Calcifying pseudoneoplasms of the neuraxis: CT, MR imaging, and histologic features. AJNR Am J Neuroradiol 2009;30(6):1256‐1260. ; Bähr O, Hattingen E, Rieger J, Steinbach JP. Bevacizumab‐induced tumor calcifications as a surrogate marker of outcome in patients with glioblastoma. Neuro Oncol 2011;13(9):1020‐1029. ; Remes TM, Suo‐Palosaari MH, Koskenkorva PKT, et al. Radiation‐induced accelerated aging of the brain vasculature in young adult survivors of childhood brain tumors. Neurooncol Pract 2020;7(4):415‐427. ; Metoki T, Mugikura S, Higano S, et al. Subcortical calcification on CT in dural arteriovenous fistula with cortical venous reflux. AJNR Am J Neuroradiol 2006;27(5):1076‐1078. ; Yang MS, Chen CC, Cheng YY, Yeh DM, Lee SK, Tyan YS. Unilateral subcortical calcification: A manifestation of dural arteriovenous fistula. AJNR Am J Neuroradiol 2005;26(5):1149‐1151. ; Stapleton CJ, Barker FG II. Cranial cavernous malformations: Natural history and treatment. Stroke 2018;49(4):1029‐1035. ; Tan H, Liu T, Wu Y, et al. Evaluation of iron content in human cerebral cavernous malformation using quantitative susceptibility mapping. Invest Radiol 2014;49(7):498‐504. ; Perez‐Malagon CD, Barrera‐Rodriguez R, Lopez‐Gonzalez MA, Alva‐Lopez LF. Diagnostic and neurological overview of brain tuberculomas: A review of literature. Cureus 2021;13(12):e20133. ; Benson JC, Cervantes G, Baron TR, et al. Imaging features of neurotoxoplasmosis: A multiparametric approach, with emphasis on susceptibility‐weighted imaging. Eur J Radiol Open 2018;5:45‐51. ; Kanda T, Ishii K, Kawaguchi H, Kitajima K, Takenaka D. High signal intensity in the dentate nucleus and globus pallidus on unenhanced T1‐weighted MR images: Relationship with increasing cumulative dose of a gadolinium‐based contrast material. Radiology 2014;270(3):834‐841. ; Nakamichi R, Taoka T, Kawai H, Yoshida T, Sone M, Naganawa S. Magnetic resonance cisternography imaging findings related to the leakage of gadolinium into the subarachnoid space. Jpn J Radiol 2021;39(10):927‐937. ; Hinoda T, Fushimi Y, Okada T, et al. Quantitative assessment of gadolinium deposition in dentate nucleus using quantitative susceptibility mapping. J Magn Reson Imaging 2017;45(5):1352‐1358. ; Choi Y, Jang J, Kim J, et al. MRI and quantitative magnetic susceptibility maps of the brain after serial Administration of Gadobutrol: A longitudinal follow‐up study. Radiology 2020;297(1):143‐150. ; Frenzel T, Lengsfeld P, Schirmer H, Hutter J, Weinmann HJ. Stability of gadolinium‐based magnetic resonance imaging contrast agents in human serum at 37 degrees C. Invest Radiol 2008;43(12):817‐828. ; Remacha A, Sanz C, Contreras E, et al. Guidelines on haemovigilance of post‐transfusional iron overload. Blood Transfus 2013;11(1):128‐139. ; Hasiloglu ZI, Asik M, Ure E, Ertem F, Apak H, Albayram S. The utility of susceptibility‐weighted imaging to evaluate the extent of iron accumulation in the choroid plexus of patients with beta‐thalassaemia major. Clin Radiol 2017;72(10):903.e1‐903.e7. ; Manara R, Ponticorvo S, Tartaglione I, et al. Brain iron content in systemic iron overload: A beta‐thalassemia quantitative MRI study. Neuroimage Clin 2019;24:102058. ; Dimov AV, Li J, Nguyen TD, et al. QSM throughout the body. J Magn Reson Imaging 2023;57(6):1621‐1640. ; Fushimi Y, Yoshida K, Okawa M, et al. Vessel wall MR imaging in neuroradiology. Radiol Med 2022;127(9):1032‐1045. ; Wang C, Zhang Y, Du J, et al. Quantitative susceptibility mapping for characterization of intraplaque hemorrhage and calcification in carotid atherosclerotic disease. J Magn Reson Imaging 2020;52(2):534‐541. ; Ikebe Y, Ishimaru H, Imai H, et al. Quantitative susceptibility mapping for carotid atherosclerotic plaques: A pilot study. Magn Reson Med Sci 2020;19(2):135‐140. ; Stone AJ, Tornifoglio B, Johnston RD, Shmueli K, Kerskens C, Lally C. Quantitative susceptibility mapping of carotid arterial tissue ex vivo: Assessing sensitivity to vessel microstructural composition. Magn Reson Med 2021;86(5):2512‐2527. ; Sharma SD, Hernando D, Horng DE, Reeder SB. Quantitative susceptibility mapping in the abdomen as an imaging biomarker of hepatic iron overload. Magn Reson Med 2015;74(3):673‐683. ; Tipirneni‐Sajja A, Loeffler RB, Hankins JS, Morin C, Hillenbrand CM. Quantitative susceptibility mapping using a multispectral autoregressive moving average model to assess hepatic iron overload. J Magn Reson Imaging 2021;54(3):721‐727. ; Yokoo T, Browning JD. Fat and iron quantification in the liver: Past, present, and future. Top Magn Reson Imaging 2014;23(2):73‐94. ; Albano D, Bruno F, Agostini A, et al. Dynamic contrast‐enhanced (DCE) imaging: State of the art and applications in whole‐body imaging. Jpn J Radiol 2022;40(4):341‐366. ; Straub S, Laun FB, Emmerich J, et al. Potential of quantitative susceptibility mapping for detection of prostatic calcifications. J Magn Reson Imaging 2017;45(3):889‐898.
  • Grant Information: JP21K15623 Japan Society for the Promotion of Science; JP21K15826 Japan Society for the Promotion of Science; JP22K07746 Japan Society for the Promotion of Science
  • Contributed Indexing: Keywords: computed tomography; magnetic resonance imaging; neuroradiology; quantitative susceptibility mapping
  • Entry Date(s): Date Created: 20230908 Date Completed: 20240509 Latest Revision: 20240516
  • Update Code: 20240517

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