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REVIEW ARTICLE Quantitative MRI in Multiple Sclerosis: From Theory to Application M. Tranfa, G. Pontillo, M. Petracca, A. Brunetti, E. Tedeschi, G. Palma, and S. Cocozza ABSTRACT SUMMARY: Quantitative MR imaging techniques allow evaluating different aspects of brain microstructure, providing meaningful in- formation about the pathophysiology of damage in CNS disorders. In the study of patients with MS, quantitative MR imaging tech- niques represent an invaluable tool for studying changes in myelin and iron content occurring in the context of inflammatory and neurodegenerative processes. In the first section of this review, we summarize the physics behind quantitative MR imaging, here defined as relaxometry and quantitative susceptibility mapping, and describe the neurobiological correlates of quantitative MR imaging findings. In the second section, we focus on quantitative MR imaging application in MS, reporting the main findings in both the gray and white matter compartments, separately addressing macroscopically damaged and normal-appearing parenchyma. ABBREVIATIONS: bSSFP ¼ balanced steady-state free precession; CL ¼ cortical lesions; GRE ¼ gradient recalled-echo; NAWM ¼ normal-appearing white matter; PD ¼ proton density; qMRI ¼ quantitative MR imaging; QSM ¼ quantitative susceptibility mapping; RF ¼ radiofrequency 9 10 hile conventional MR imaging plays an unquestionable calcifications, and to evaluate cortical bone mineral density or 1,2 Wrole in the diagnosis and management of MS, it myocardial structural alterations. offers very limited information about the pathophysiology of Although the definition of qMRI is open to different interpre- tissue damage because conventional sequences are not able to tations, several advanced MR imaging techniques are usually detect subtle changes affecting WM and GM. Quantitative grouped under this umbrella, including relaxometry, magnetic MR imaging (qMRI) bridges this gap, detecting brain micro- susceptibility, diffusion invariants, magnetization transfer, and, structural alterations with high sensitivity and robustness to 12 to some extent, perfusion parameters. Each of these techniques interscanner and interobserver variability, thus providing offers different, sometimes complementary, insights into the measures that can be compared among sites and longitudinal 13 complex tissue alterations occurring in MS. In this light, it is examinations. Furthermore, this technique has been success- noteworthy to remember that, while demyelination represents fully used to differentiate MS from other demyelinating dis- the end result of a complex phenomenon of inflammation, ulti- eases, such as neuromyelitis optica, which presents a different mately leading to axonal and neuronal degeneration, change in spectrum of relaxometry alterations and a peculiar spatial iron homeostasis is a crucial step in the pathophysiology of dam- deep gray matter involvement, and alsotocharacterize other age in MS, linked to microglial activation and modifications in conditions with different etiologies, from vascular disease to 14,15 oligodendrocyte functionality. Relaxometry plays a unique 5,6 brain tumors. However, the applications of qMRI extend role, given that most of the above-mentioned qMRI techniques beyond the brain, being able to depict changes in liver iron offer valuable and sensitive tools in myelin assessment but they 7 8 concentration as well as the presence of fibrosis, and prostatic lack iron-detection sensitivity. Indeed, relaxometry assesses abnormalities of iron and myelin, elements that are at the cross- Received January 12, 2022; accepted after revision February 22. roads of the inflammatory and neurodegenerative components in From the Departments of Advanced Biomedical Sciences (M.T., G. Pontillo, A.B., E.T., S.C.), MS pathophysiology. and Electrical Engineering and Information Technology (G. Pontillo), University of Naples In this review, wesummarize theroleand theapplication “Federico II,” Naples, Italy; Department of Human Neurosciences (M.P.), Sapienza University of Rome, Rome, Italy; and Institute of Nanotechnology (G. Palma), National of qMRI techniques, here defined as relaxometry (estimating Research Council, Lecce, Italy. R1, R2, R2*, and, by extension, proton density [PD]) and Please address correspondence to Giuseppe Pontillo, MD, Department of quantitative susceptibility mapping (QSM), to the study of Advanced Biomedical Sciences, University “Federico II,” Via Pansini, 5, 80131 Naples, Italy; e-mail: giuseppe.pontillo@unina.it; @NeuroN_Lab patients with MS. Indicates open access to non-subscribers at www.ajnr.org In the first section, we briefly describe the physics behind http://dx.doi.org/10.3174/ajnr.A7536 qMRI, together with its neurobiological correlates. In the second 1688 Tranfa Dec 2022 www.ajnr.org FIG 1. An example of quantitative MR imaging maps. Along with findings of a conventional FLAIR sequence (A) are examples of R1 (B), PD (C), R2* (D), and QSM (E) maps from a 22 -year-old man with MS. section, we summarize brain qMRI findings in MS for both the the Ernst angle for SNR convenience and, therefore, can be esti- normal-appearing parenchyma and lesions in the GM and WM mated on the basis of the same protocol structure adopted for R1 compartments. mapping. Once R1 and R2* (which rule the signal equation of the spoiled GRE sequence) have been obtained, PD is ideally obtained without qMRI Theory further acquisitions. Nevertheless, the spatial sensitivity of the re- Impact of Excitation Pulses and Significance of 3D Sequences. ceiver coil for the brain is substantially inhomogeneous; therefore, The R1 and R2 relaxation rates, defined as the inverses of T1 and an additional low-resolution acquisition of one of the sequences T2 relaxation times, measure the efficiency of the kinetics mecha- with the body coil helps to mitigate the inhomogeneity bias. nisms restoring the thermal equilibrium of the longitudinal and Finally, the phase of the complex images acquired for R2* transverse components of the spin isochromats. An isochromat mapping permits QSM. The raw phase is first unwrapped and represents the magnetic moment associated with a subset of nuclei then filtered to remove the background component that is not (protons, for our purposes) whose cardinality is large enough to associated with the local magnetization induced in the paren- justify a classic description of its dynamics (in terms of the expec- chyma by the main magnetic field. The filtered phase is finally tation value of the quantum magnetic moment operator) and processed to solve the inverse problem leading to the QSM. In whose spatial extent is small enough to assume a strictly uniform this step, special care must be taken to avoid the occurrence of macroscopic magnetic field throughout the subset. The evolution streaking artifacts that could impact the clinical value of the of the isochromats in an MR imaging sequence (radiofrequency image by mimicking spurious anatomic structures (Fig 1). [RF] and gradient pulses) is strongly dependent on the flip angles they experience. This shows why accurate R1 and R2 mapping is Importance of Denoising Schemes. The mathematic problems only possible through 3D sequences, which, unlike 2D sequences, associated with the qMRI protocols are typically ill-conditioned, guarantee a roughly uniform RF excitation throughout each voxel. thus leading to a detrimental noise propagation from the acquired images to the reconstructed maps. Therefore, besides the custom- Estimation of Quantitative Maps. In general, theviableprotocols ary optimization of the acquisition protocol to maximize the SNR for R1 and R2 mapping in neuroimaging routine rely on the ac- of the quantitative maps, a denoising step is warranted upstream quisition of multiple 3D spoiled gradient recalled echo (GRE, for of the qMRI pipeline. In this context, multispectral versions of the R1) and balanced steady-state free precession (bSSFP, for the non-local means algorithms have been devised to account for the additional information required to estimate R2) sequences at vari- power distribution of noise in parallel imaging and to reconstruct able flip angles. However, several aspects need to be considered the true signal from the raw statistical moments of the acquired 25,26 to obtain accurate relaxation maps. First, nonideal slab profiles images. can be accounted for with a dedicated sequence for flip angle mapping or through an iterative approach based on the infor- Pathophysiologic Correlates of qMRI. The pathophysiology of mation content of the estimated relaxation maps. The bias from brain damage in MS is multifaceted, being characterized by a nonideal RF spoiling can be removed according to the specific sequence of demyelination and partial remyelination events asso- 19 27 phase increment implemented by each vendor. Finally, to factor ciated with neurodegeneration. out the effects of off-resonance phenomena impacting the bSSFP Microglia activation within normal-appearing WM (NAWM) images in the form of banding artifacts, one needs to adopt a is one of the earliest and most prominent features in MS patho- 18 28 modified version of the original bSSFP approach, based on a physiology. Subsequently, a loss of integrity of the blood-brain synthetic contrast from multiple phase-cycled bSSFP. barrier, driven by proinflammatory mediators produced by resi- The estimation of the free induction decay rate (R2*) is compa- dent and endothelial cells, as well as indirect leukocyte-dependent ratively simpler because it depends only on the ratios of the signals damage, leads to focal demyelination. As the disease progresses, at different TEs, with no RF pulses in between. It is usually oligodendrocyte depletion occurs, as well as oligodendroglial iron obtained through a multi-GRE sequence with flip angles close to release, secondary to the high concentration of proinflammatory AJNR Am J Neuroradiol 43:1688–95 Dec 2022 www.ajnr.org 1689 FIG 2. Conventional and quantitative MR imaging findings of WM lesions at different stages. In the upper row, conventional findings (postgadoli- nium T1-weighted and precontrast T2-weighted, first and second images from left to right respectively) of a typical pattern of nodular enhance- ment in an early active lesion (arrows) showing isointense signal in QSM (third image, white box) and mild hypointensity in R2* map (fourth image). In late active lesions (middle row, arrows), a peripheral pattern of enhancement is present, coupled with an area of increased signal at QSM and a slightly more pronounced hypointensity on R2* maps compared with the previous stage. As lesion staging further increases, the lesion eventually enters its chronic inactive stage (lower row, arrows), characterized by absent gadolinium enhancement, a QSM hyperintensity, and a hypointense R2* signal. Modified with permission from Zhang et al. cytokines produced by the chronically activated microglia, with WM Lesions these mechanisms ultimately resulting in oxidative stress via Fenton Focal WM lesions represent the most typical expression of tissue chemistry and reduced regenerative capacity. damage in MS. According to their activity phase, WM lesions can Because these different microstructural changes influence be histologically subcategorized as early active, late active, chronic multiple MR imaging contrasts contemporarily, multiparameter active (also described as slowly expanding or smoldering lesions), qMRI represents the most apt approach to explore pathologic chronic inactive, and shadow plaques (remyelinated lesions). alterations occurring in the MS brain. The undeniable advantage In early active lesions, inflammatory activity blooms from ven- of qMRI relies on the possibility of generating spatial maps in ules, following blood-brain barrier disruption and immune cell infil- which each voxel corresponds to a numeric value reflecting the tration, thus leading to progressive demyelination and axonal loss physical properties of the examined tissues, such as free water with a centrifugal spread. From an MR imaging perspective, these proportion (PD, R1, R2), myelination (R1, R2, R2*, QSM), or phenomena are mirrored by the pattern of enhancement after gado- 31,32 iron content (R2* and QSM). linium administration. Indeed, at this stage, lesions usually enhance While PD is an established measure of the brain free water centrifugally, with a more pronounced nodular appearance. As pool, with PD increase documented in the presence of vasogenic inflammation proceeds, cellular infiltrates grow and, combined with edema, R1 and R2 vary as a function of free water and myelin myelin breaking down and edema, result in decreased R1 and R2 concentration, with a higher degree of myelination causing relaxa- values, coupled to increased PD values within lesions and transi- 35,36 tion time shortening. tional values in periplaque WM in comparison with NAWM. With reference to iron, in normal brain tissue, it is mostly These findings are associated with a similar edema-driven R2* boundto ferritininoligodendrocytes, and its presence is required decrease, with no QSM changes because the loss of diamagnetic for the activity of enzymes involved in myelin production and pres- myelin is not detectable at this stage (Fig 2). ervation. Along with myelin, iron accounts for the larger part of In late active lesions, showing a peripheral or ringlike pattern of 38 42 the MR imaging contrast obtained through R2* and QSM. enhancement, myelin degradation and removal become progres- However, whereas both iron and myelin determine an R2* increase, sively more substantial, therefore influencing lesion magnetic sus- 45,46 they play opposite roles in QSM. Given the paramagnetic properties ceptibility as assessed by QSM. At this stage, R1, R2, and PD of iron, an increase in its concentration is unequivocally coupled to values show the same pattern of changes as the early active lesions an increase in susceptibility, while myelin, being a diamagnetic in comparison with NAWM, while in R2*, a further signal decrease compound, influences susceptibility in the opposite direction. is present, coupled to a QSM increase, especially in the lesion 1690 Tranfa Dec 2022 www.ajnr.org Table 1: Major qMRI findings in MS—WM compartment in iron homeostasis, can be assessed Site Pathologic Processes and qMRI Correlates through relaxation and magnetic suscep- 24,38 WM lesions tibility variations. Early active Decreased R1, R2, and R2* values, along with increased PD, The NAWM usually shows lower R1 43,45 reflecting initial myelin degradation and edema and R2and higher PD values,compared 50,51 Late active Decreased R1 and R2 values, coupled with increased PD, with with the WM of healthy controls. myelin debris removal that determines further R2* decrease and These changes seem to be mostly related 43,45,51 QSM increase to inflammatory infiltration, with edema Chronic active Further R1 and R2 decrease, with PD increase, due to demyelination and myelin loss. A decrease in iron progression; increased R2* and QSM at the periphery of the concentration has been observed in 45,46 lesions due to iron-laden microglia and macrophages patients with MS in comparison with Chronic inactive Compared with chronic active, R2* decreases with high QSM healthy controls using R2* maps. This values; across time, susceptibility values gradually become similar reduced relaxation rate might be driven to those in NAWM by iron release from oligodendrocytes 14,29 NAWM Decreased R1 and R2 values, with increased PD, compared with the during chronic inflammation. Most 50,51 WM of healthy controls, reflecting edema and myelin loss interesting, the iron level in NAWM, secondary to inflammatory infiltration. estimated by QSM, is not stationary but During active phases of the disease, iron is released from fluctuates according to the presence of oligodendrocytes and begins to accumulate in newly forming 52 inflammatory activity. Indeed, during lesions, causing an R2* decrease and no relevant modification 52 theactivephases of the disease, when of QSM signal iron begins to accumulate in newly forming lesions, NAWM magnetic sus- ceptibility values appear to be similar to 45 52 center, due to additional myelin debris removal by anti-inflam- those observed in the WM of healthy controls, as also confirmed 14 29 matory M2 macrophages. Although iron begins concentrating in by ex vivo data. On the contrary, mean QSM values of the M1 macrophages and activated microglia at a later stage, its levels NAWM seem to increase in the absence of gadolinium-enhancing may acutely increase following rapid oligodendrocyte destruction, lesions, suggesting that iron might play a role in tissue regeneration counterbalancing myelin loss in R2* and reinforcing QSM hyper- during periods of disease inactivity. intensity in some lesions (Fig 2). Main qMRI findings in the WM compartment are reported in When blood-brain barrier damage is resolved, MS lesions no Table 1. longer show postgadolinium enhancement and are, therefore, cate- Deep Gray Matter gorized as chronic, further subdivided into active or inactive, The major structures of the deep gray matter nuclei can be ana- depending on whether some degree of inflammatory activity per- tomically and functionally subdivided in the thalamus and basal sists. In chronic lesions, the combination of demyelination, hypo- ganglia, whose most relevant nuclei are the globus pallidus, puta- cellularity, and free water fraction increase leads to R1 and R2 men, and caudate nucleus. Given their relatively different histol- decrease, while PD increases, compared with early and late active ogy, the thalamus and basal ganglia will be discussed separately. lesions. Transition to chronicity is associated with a complex pat- tern of changes in iron content. Indeed, while iron concentration Thalamus. Thalamic involvement in MS has been documented by may decrease due to myelin sheaths and oligodendrocyte deple- 53-55 53 29,37 both ex- and in vivo studies. This region is not only a site of pri- tion, some degree of iron accumulation occurs, in parallel, mary axonal damage, but given its high interconnectivity with other within iron-laden macrophages and microglia at lesions bor- 14,32 brain regions, it suffers from secondary degeneration caused by WM ders. In chronic active lesions, this inflammation-related iron 53,54 lesions involving thalamic projection fibers. Recently, a decrease accumulation at the rim of the lesions is emphasized, leading to 56-61 45,46 in both thalamic iron content and concentration has been docu- increasedR2* andQSM values. With time, lesions eventually mented in patients with MS in comparison with healthy controls, become chronic inactive or shadow plaques, with low R2* values 59,61 with the most evident changes detected within the pulvinar. but still high QSM signal, which only ultimately decreases in very 62-66 Previous studies, however, reported conflicting results, late stages to resemble NAWM signal, due to iron depletion and only partially ascribable to the physiologic nonlinear trajectory fol- partial remyelination (Fig 2). lowed by thalamic iron concentration during the life span. Such NAWM conflicting data should be interpreted considering the impact of at- 57,68 Despite appearing spared by lesions on conventional MR imaging rophyonironconcentration. In particular, the concept of R2* sequences, NAWM is characterized by complex microstructural mass (the sum of all the R2* values in a specific region) was changes reflecting inflammation, demyelination, gliosis, and axonal recently introduced as an index of iron content independent of at- loss. The mechanisms underlying NAWM damage are mainly rophy. With this approach, the decrease in thalamic iron content Wallerian degeneration of fibers transected by focal lesions and dif- has been confirmed, highlighting the importance of distinguish- 28,48 49 fuse microglial activation. Axonal swelling and edema have ingbetween (and reportingboth) iron concentration and content 56,57,59,60 also been observed globally in NAWM and, together with alteration (Fig 3). AJNR Am J Neuroradiol 43:1688–95 Dec 2022 www.ajnr.org 1691 Similar to what we described for the thalamus, the progressive damage of the basal ganglia leads to atrophy. Here, studies have 58,64-66,70,71 more consistently reported an increase in R2* or sus- 61,64,66,70,72 ceptibility in patients with MS compared with controls, suggesting a progressive iron accumulation, beyond the analogous physiologic process detectable in healthy individuals. Nonetheless, these findings should also be interpreted in view of the effect of atrophy on tissue iron concentration. Indeed, even with stable regional iron content, volume reduction leads to increased mean iron concentration. In particular, a prominent decline in iron content with time in all basal ganglia has been demonstrated, coupled with an increased or stable iron concen- tration compared with controls at the level of putamen, caudate 60,72 nucleus, and globus pallidus. In line with these results, some recent studies failed to identify any difference between patients with MS and controls in terms of iron content, while others reported a decrease of this parameter in the putamen and caudate 56,57 nucleus of patients with MS (Fig 3). Cortical Lesions From a relaxometry perspective, no studies have investigated R1 changes in cortical lesions (CL). However, beyond demyelination, CL are characterized by a decreased iron load, a feature that allows differentiating them from a normal-appearing cortex through the 74,75 evaluation of R2* maps, as shown in postmortem samples. In particular, the progressive destruction of iron-rich myelin sheaths and oligodendrocytes and the subsequent uptake of iron and myelin debris by activated macrophages and microglia lead to decreased R2* values in CL. On the other hand, QSM has been used to analyze the heterogeneity of CL in different disease stages, showing a mixed pattern of appearance. While QSM- hyperintense CL have been more frequently observed in patients with relapsing-remitting MS, QSM-hypointense CL are mostly identified in subjects with a secondary-progressive phenotype. While the increased susceptibility might be due to iron release from oligodendrocytes, typical of the inflammatory phase of the disease, the reduced susceptibility might be linked to iron deple- tion in chronic lesions. Normal-Appearing Cortex Similar to the NAWM, the cortex, which does not show signal changes on conventional MR imaging, is subject, from a patho- logic standpoint, to neuronal and axonal loss occurring regardless 76,79 of demyelination. The assessment of relaxometry and QSM changes in normal-appearing cortex is confounded by the physio- logic layer-specific iron content, which represents the main FIG 3. Pattern of iron concentration, iron content, and myelin con- 81 36 tent changes in deep gray matter nuclei in MS. Results of voxelwise source of cortical R2* and susceptibility contrast. Nevertheless, analyses comparing patients with MS with healthy controls, showing a decrease in both R1 and R2* values has been reported in MS in the presence of an increased iron concentration at the level of the normal-appearing cortex, accounting for demyelination and iron basal ganglia (red-yellow), coupled with a decrease in iron and myelin depletion, respectively. Consistent with the hypothesis of cortical content mainly affecting the thalami and, in particular, the pulvinar nuclei (blue-light blue). Modified with permission from Pontillo et demyelination triggered by chemokines produced by lymphocytic al. HC indicates healthy control; 1-r, 1 minus P value. infiltrates in the meningeal compartment, a recent study has reported coherent cortical gradients of R1 and R2*, oriented from the subpial layer to the WM interface. In thesamestudy, QSM Basal Ganglia. The basal ganglia are also the site of both demye- showed a lack of sensitivity in distinguishing the different layers, lination and neurodegeneration, with reduced neuronal density, probably due to the counteracting effects of diamagnetic myelin 44,55 axonal damage, and oligodendrocytes loss. and paramagnetic iron modifications. 1692 Tranfa Dec 2022 www.ajnr.org Table 2: Major qMRI findings in MS—GM compartment of liver iron concentration. JMagn Reson Imaging 2018;48:1069–79 CrossRef Site Pathologic Processes and qMRI Correlates Medline Deep gray matter 58,64-66,70,71 61,64,66,70,72 8. Banerjee R, Pavlides M, Tunnicliffe EM, Basal ganglia Increased R2* and QSM values indicating et al. Multiparametric magnetic reso- increased iron concentrations, with atrophy that might play a 57,68 nance for the non-invasive diagnosis role in causing these changes of liver disease. JHepatol 2014;60:69–77 Thalamus The more recent findings suggest the presence of reduced iron CrossRef Medline content and concentration, along with demyelination, as shown 9. Straub S, Laun FB, Emmerich J, et al. 56-61 by decreased R1, R2*, and QSM values Potential of quantitative susceptibility Cortical gray matter mapping for detection of prostatic cal- 74,75 Cortical lesions Reduced R2* signal due to demyelination and iron depletion; cifications. JMagn Reson Imaging cortical lesions are more heterogeneous on QSM, with 2017;45:889– 98 CrossRef Medline decreased-to-increased values, depending on the level of 10. JerbanS,Lu X,Jang H,et al. Significant inflammatory activity correlations between human cortical bone mineral density and quantitative Normal-appearing Demyelination and iron depletion lead to reduced R1 and R2* 50 83 susceptibility mapping (QSM) obtained cortex values, with a gradient indicating the WM interface with 3D cones ultrashort echo time magnetic resonance imaging (UTE- MRI). Magn Reson Imaging 2019;62:104– 10 CrossRef Medline The main qMRI findings of the GM compartment are 11. Karur GR, Hanneman K. Cardiac MRI T1, T2, and T2* mapping in clinical practice. Advances in Clinica Radiology 2019;1:27–41 CrossRef reported in Table 2. 12. Granziera C, Wuerfel J, Barkhof F, et al; MAGNIMS Study Group. Quantitative magnetic resonance imaging towards clinical applica- CONCLUSIONS tion in multiple sclerosis. Brain 2021;144:1296–1311 CrossRef Medline In this review, we offered a comprehensive overview of qMRI 13. Pontillo G, Cocozza S, Lanzillo R, et al. Determinants of deep gray matter atrophy in multiple sclerosis: a multimodal MRI study. applications in MS, while also describing the theory behind map AJNR Am J Neuroradiol 2019;40:99–106 CrossRef Medline generation and the most likely histologic correlates of qMRI find- 14. Mehta V, Pei W,Yang G,et al. Iron is a sensitive biomarker for ings. The multiparameter nature of qMRI has already allowed inflammation in multiple sclerosis lesions. PLoS One 2013;8:e57573 researchers to gain additional, valuable insights about the multifac- CrossRef Medline eted pathophysiology of brain damage in MS. Given the increasing 15. Möller HE, Bossoni L, Connor JR, et al. Iron, myelin, and the brain: accessibility to quantitative sequences on novel MR imaging scan- neuroimaging meets neurobiology. Trends Neurosci 2019;42:384– ners, in the near future, qMRI will also likely play a fundamental 401 CrossRef Medline 16. Deoni SC, Rutt BK, Peters TM. Rapid combined T1 and T2 map- role in clinical practice as a sensitive tool to quantitatively assess ping using gradient recalled acquisition in the steady state. Magn brain damage in patients with MS, with relevant implications for Reson Med 2003;49:515–26 CrossRef Medline prognostic stratification and treatment-response evaluation. 17. Yarnykh VL. Actual flip-angle imaging in the pulsed steady state: a method for rapid three-dimensional mapping of the transmitted ra- Disclosure forms provided by the authors are available with the full text and diofrequency field. Magn Reson Med 2007;57:192–200 CrossRef Medline PDF of this article at www.ajnr.org. 18. Palma G, Tedeschi E, Borrelli P, et al. A novel multiparametric approach to 3D quantitative MRI of the brain. PLoS One 2015;10: e0134963 CrossRef Medline REFERENCES 19. Baudrexel S, Nöth U, Schüre JR, et al. T mapping with the variable 1. Rovira À, Wattjes MP, Tintoré M, et al; MAGNIMS Study Group. 1 flip angle technique: a simple correction for insufficient spoiling Evidence-based guidelines: MAGNIMS consensus guidelines on the of transverse magnetization. Magn Reson Med 2018;79:3082–92 use of MRI in multiple sclerosis-clinical implementation in the diag- CrossRef Medline nostic process. Nat Rev Neurol 2015;11:471–82 CrossRef Medline 20. Björk M, Ingle RR, Gudmundson E, et al. Parameter estimation 2. Wattjes MP, Ciccarelli O, Reich DS, et al; North American Imaging in approach to banding artifact reduction in balanced steady-state Multiple Sclerosis Cooperative MRI Guidelines Working Group. free precession. Magn Reson Med 2014;72:880–92 CrossRef Medline MAGNIMS-CMSC-NAIMS consensus recommendations on the use of MRI in patients with multiple sclerosis. Lancet Neurol 2021;20:653– 21. Monti S, Borrelli P, Tedeschi E, et al. RESUME: turning an SWI ac- 70 CrossRef Medline quisition into a fast qMRI protocol. PLoS One 2017;12:e0189933 CrossRef Medline 3. Hagiwara A, Otsuka Y, Andica C, et al. Differentiation between multiple sclerosis and neuromyelitis optica spectrum disorders by 22. Monti S, Pontillo G, Russo C, et al. RESUME :a flexible class of multi-parameter qMRI protocols. Phys Med 2021;88:23–36 CrossRef multiparametric quantitative MRI using convolutional neural net- work. JClin Neurosci 2021;87:55–58 CrossRef Medline Medline 4. Pudlac A, Burgetova A, Dusek P, et al. Deep gray matter iron con- 23. Li W,Wang N,Yu F,et al. A method for estimating and removing tent in neuromyelitis optica and multiple sclerosis. BioMed Res Int streaking artifacts in quantitative susceptibility mapping. Neuroimage 2020;2020:6492786 CrossRef Medline 2015;108:111–22 CrossRef Medline 5. Deistung A, Schweser F, Reichenbach JR. Overview of quantitative sus- 24. Li W, Wu B, Liu C. Quantitative susceptibility mapping of human ceptibility mapping. NMR Biomed 2017;30:e3569 CrossRef Medline brain reflects spatial variation in tissue composition. Neuroimage 6. Seiler A, Nöth U, Hok P, et al. Multiparametric quantitative MRI in 2011;55:1645–56 CrossRef Medline neurological diseases. Front Neurol 2021;12:640239 CrossRef Medline 25. Aja-Fernández S, Pieciak T, Vegas-Sánchez-Ferrero G. Spatially vari- 7. Li J, Lin H, Liu T, et al. Quantitative susceptibility mapping (QSM) ant noise estimation in MRI: a homomorphic approach. Med Image minimizes interference from cellular pathology in R2* estimation Anal 2015;20:184–97 CrossRef Medline AJNR Am J Neuroradiol 43:1688–95 Dec 2022 www.ajnr.org 1693 26. Borrelli P, Palma G, Tedeschi E, et al. Improving signal-to-noise ra- 47. Harrison DM, Li X, Liu H, et al. Lesion heterogeneity on high-field tio in susceptibility weighted imaging: a novel multicomponent susceptibility MRI is associated with multiple sclerosis severity. non-local approach. PLoS One 2015;10:e0126835 CrossRef Medline AJNR Am J Neuroradiol 2016;37:1447–53 CrossRef Medline 27. Filippi M, Bar-Or A, Piehl F, et al. Multiple sclerosis. Nat Rev Dis 48. Dziedzic T, Metz I, Dallenga T, et al. Wallerian degeneration: a major Primers 2018;4:43 CrossRef Medline component of early axonal pathology in multiple sclerosis. Brain 28. Allen IV, McQuaid S, Mirakhur M, et al. Pathological abnormalities Pathol 2010;20:976–85 CrossRef Medline in the normal-appearing white matter in multiple sclerosis. Neurolo 49. Filippi M, Rocca MA, Barkhof F, et al; Attendees of the Correlation Sci 2001;22:141–44 CrossRef Medline between Pathological MRI Findings in MS Workshop. Association 29. HametnerS, WimmerI, HaiderL, et al. Iron and neurodegeneration between pathological and MRI findings in multiple sclerosis. Lancet in the multiple sclerosis brain. Ann Neurol 2013;74:848–61 CrossRef Neurol 2012;11:349–60 CrossRef Medline Medline 50. Lommers E, Simon J, Reuter G, et al. Multiparameter MRI quantifi- 30. Stephenson E, Nathoo N, Mahjoub Y, et al. Iron in multiple sclerosis: cation of microstructural tissue alterations in multiple sclerosis. roles in neurodegeneration and repair. Nat Rev Neurol 2014;10:459–68 Neuroimage Clin 2019;23:101879 CrossRef Medline CrossRef Medline 51. West J, Aalto A, Tisell A, et al. Normal-appearing and diffusely 31. Weiskopf N, Edwards LJ, Helms G, et al. Quantitative magnetic res- abnormal white matter in patients with multiple sclerosis assessed onance imaging of brain anatomy and in vivo histology. Nat Rev with quantitative MR. PLoS One 2014;9:e95161 CrossRef Medline Phys 2021;3:570–88 CrossRef 52. Chen W, Zhang Y, Mu K, et al. Quantifying the susceptibility varia- 32. Wisnieff C, Ramanan S, Olesik J, et al. Quantitative susceptibility map- tion of normal-appearing white matter in multiple sclerosis by quan- ping (QSM) of white matter multiple sclerosis lesions: Interpreting titative susceptibility mapping. AJR Am J Roentgenol 2017;209:889–94 positive susceptibility and the presence of iron. Magn Reson Med CrossRef Medline 2015;74:564–70 CrossRef Medline 53. Cifelli A, Arridge M, Jezzard P, et al. Thalamic neurodegeneration in 33. Tofts PS. PD: proton density of tissue water. In: Tofts PS. multiple sclerosis. Ann Neurol 2002;52:650–53 CrossRef Medline Quantitative MRI of the Brain: Measuring Changes Caused by Disease. 54. Mahajan KR, Nakamura K, Cohen JA, et al. Intrinsic and extrinsic Wiley Online Library; 2003:85–109 mechanisms of thalamic pathology in multiple sclerosis. Ann Neurol 34. Eis M, Els T, Hoehn-Berlage M. High resolution quantitative relaxa- 2020;88:81–92 CrossRef Medline tion and diffusion MRI of three different experimental brain 55. Vercellino M, Masera S, Lorenzatti M, et al. Demyelination, inflam- tumors in rat. Magn Reson Med 1995;34:835–44 CrossRef Medline mation, and neurodegeneration in multiple sclerosis deep gray 35. Schmierer K, Wheeler-Kingshott CA, Tozer DJ, et al. Quantitative matter. J Neuropathol Exp Neurol 2009;68:489–502 CrossRef Medline magnetic resonance of postmortem multiple sclerosis brain before 56. Elkady AM, Cobzas D, Sun H, et al. Five year iron changes in relapsing- and after fixation. Magn Reson Med 2008;59:268–77 CrossRef Medline remitting multiple sclerosis deep gray matter compared to healthy 36. Stüber C, Morawski M, Schäfer A, et al. Myelin and iron concentra- controls. Mult Scler Relat Disord 2019;33:107–15 CrossRef Medline tion in the human brain: a quantitative study of MRI contrast. Neuroimage 2014;93 Pt 1:95–106 CrossRef Medline 57. Hernández-Torres E, Wiggermann V, Machan L, et al. Increased mean 37. Bagnato F, Hametner S, Yao B, et al. Tracking iron in multiple scle- R2* in the deep gray matter of multiple sclerosis patients: have we rosis: a combined imaging and histopathological study at 7 Tesla. been measuring atrophy? J Magn Reson Imaging 2019;50:201–08 Brain 2011;134:3602–15 CrossRef Medline CrossRef Medline 38. Hametner S, Endmayr V, Deistung A, et al. The influence of brain 58. Khalil M, Langkammer C, Pichler A, et al. Dynamics of brain iron iron and myelin on magnetic susceptibility and effective transverse levels in multiple sclerosis: a longitudinal 3T MRI study. Neurology relaxation: a biochemical and histological validation study. 2015;84:2396–2402 CrossRef Medline Neuroimage 2018;179:117–33 CrossRef Medline 59. Pontillo G, Petracca M, Monti S, et al. Unraveling deep gray matter 39. van der Valk P, De Groot CJ. Staging of multiple sclerosis (MS) lesions: atrophy and iron and myelin changes in multiple sclerosis. AJNR pathology of the time frame of MS. Neuropathol Appl Neurobiol Am J Neuroradiol 2021;42:1223–30 CrossRef Medline 2000;26:2–10 CrossRef Medline 60. Schweser F, Hagemeier J, Dwyer MG, et al. Decreasing brain iron in 40. Frischer JM, Weigand SD, Guo Y, et al. Clinical and pathological multiple sclerosis: the difference between concentration and content insights into the dynamic nature of the white matter multiple scle- in iron MRI. Hum Brain Mapp 2021;42:1463–74 CrossRef Medline rosis plaque. Ann Neurol 2015;78:710–21 CrossRef Medline 61. Zivadinov R, Tavazzi E, Bergsland N, et al. Brain iron at quantitative 41. Tallantyre EC, Brookes MJ, Dixon JE, et al. Demonstrating the perivas- MRI is associated with disability in multiple sclerosis. Radiology cular distribution of MS lesions in vivo with 7-Tesla MRI. Neurology 2018;289:487–96 CrossRef Medline 2008;70:2076–78 CrossRef Medline 62. Cobzas D, Sun H, Walsh AJ, et al. Subcortical gray matter segmenta- 42. Gaitán MI, Shea CD, Evangelou IE, et al. Evolution of the blood-brain tion and voxel-based analysis using transverse relaxation and quanti- barrier in newly forming multiple sclerosis lesions. Ann Neurol tative susceptibility mapping with application to multiple sclerosis. J 2011;70:22–29 CrossRef Medline Magn Reson Imaging 2015;42:1601–10 CrossRef Medline 43. Blystad I, Håkansson I, Tisell A, et al. Quantitative MRI for analysis of 63. Lebel RM,Eissa A, Seres P,et al. Quantitative high-field imaging of active multiple sclerosis lesions without gadolinium-based contrast sub-cortical gray matter in multiple sclerosis. Mult Scler 2012;18:433– agent. AJNR Am J Neuroradiol 2016;37:94–100 CrossRef Medline 41 CrossRef Medline 44. Hagiwara A, Hori M, Yokoyama K, et al. Utility of a multiparametric 64. Rudko DA, Solovey I, Gati JS, et al. Multiple sclerosis: improved quantitative MRI model that assesses myelin and edema for evalu- identification of disease-relevant changes in gray and white matter ating plaques, periplaque white matter, and normal-appearing by using susceptibility-based MR imaging. Radiology 2014;272:851– white matter in patients with multiple sclerosis: a feasibility study. 64 CrossRef Medline AJNR Am J Neuroradiol 2017;38:237–42 CrossRef Medline 65. Walsh AJ, Blevins G, Lebel RM, et al. Longitudinal MR imaging of 45. Zhang Y, Gauthier SA, Gupta A, et al. Quantitative susceptibility iron in multiple sclerosis: an imaging marker of disease. Radiology mapping and R2* measured changes during white matter lesion 2014;270:186–96 CrossRef Medline development in multiple sclerosis: myelin breakdown, myelin de- 66. Fujiwara E, Kmech JA, Cobzas D, et al. Cognitive implications of bris degradation and removal, and iron accumulation. AJNR Am J deep gray matter iron in multiple sclerosis. AJNR Am J Neuroradiol Neuroradiol 2016;37:1629–35 CrossRef Medline 46. Chen W, Gauthier SA, Gupta A, et al. Quantitative susceptibility 2017;38:942–48 CrossRef Medline 67. Hallgren B, Sourander P. The effect of age on the non-haemin iron mapping of multiple sclerosis lesions at various ages. Radiology 2014;271:183–92 CrossRef Medline in the human brain. Neurochem 1958;3:41–51 CrossRef Medline 1694 Tranfa Dec 2022 www.ajnr.org 68. Eshaghi A, Marinescu RV, Young AL, et al. Progression of regional 76. Peterson JW, Bö L, Mörk S, et al. Transected neurites, apoptotic grey matter atrophy in multiple sclerosis. Brain 2018;141:1665–77 neurons, and reduced inflammation in cortical multiple sclerosis CrossRef Medline lesions. Ann Neurol 2001;50:389–400 CrossRef Medline 69. Haider L, Simeonidou C, Steinberger G, et al. Multiple sclerosis deep 77. Fischer MT,Wimmer I,Höftberger R,et al. Disease-specific molecular grey matter: the relation between demyelination, neurodegeneration, events in cortical multiple sclerosis lesions. Brain 2013;136:1799–1815 inflammation and iron. J Neurol Neurosurg Psychiatry 2014;85:1386–95 CrossRef Medline CrossRef Medline 78. Castellaro M, Magliozzi R, Palombit A, et al. Heterogeneity of corti- 70. Elkady AM, Cobzas D, Sun H, et al. Progressive iron accumulation across cal lesion susceptibility mapping in multiple sclerosis. AJNR Am J multiple sclerosis phenotypes revealed by sparse classification of deep Neuroradiol 2017;38:1087–95 CrossRef Medline gray matter. JMagn Reson Imaging 2017;46:1464–73 CrossRef Medline 79. Magliozzi R, Howell OW, Reeves C, et al. A Gradient of neuronal 71. Ropele S, Kilsdonk ID, Wattjes MP, et al. Determinants of iron accu- loss and meningeal inflammation in multiple sclerosis. Ann Neurol mulation in deep grey matter of multiple sclerosis patients. Mult 2010;68:477–93 CrossRef Medline Scler 2014;20:1692–98 CrossRef Medline 80. Fukunaga M, Li TQ, van Gelderen P, et al. Layer-specific variation of 72. Hagemeier J, Zivadinov R, Dwyer MG, et al. Changes of deep gray mat- iron content in cerebral cortex as a source of MRI contrast. Proc ter magnetic susceptibility over 2 years in multiple sclerosis and healthy Natl Acad Sci U S A 2010;107:3834–39 CrossRef Medline control brain. Neuroimage Clin 2018;18:1007–16 CrossRef Medline 81. Bagnato F, Hametner S, Boyd E, et al. Untangling the R2* contrast 73. Haacke EM, Cheng NY, House MJ, et al. Imaging iron stores in the in multiple sclerosis: a combined MRI-histology study at 7.0 Tesla. brain using magnetic resonance imaging. Magn Reson Imaging PLoS One 2018;13:e0193839 CrossRef Medline 2005;23:1–25 CrossRef Medline 82. Magliozzi R, Reynolds R, Calabrese M. MRI of cortical lesions and 74. Jonkman LE,Fleysher L,Steenwijk MD, et al. Ultra-high field MTR its use in studying their role in MS pathogenesis and disease and qR2* differentiates subpial cortical lesions from normal- course. Brain Pathol 2018;28:735–42 CrossRef Medline appearing gray matter in multiple sclerosis. Mult Scler 2016;22:1306– 83. Lema Dopico A, Choi S, Hua J, et al. Multi-layer analysis of quantita- 14 CrossRef Medline tive 7 T magnetic resonance imaging in the cortex of multiple scle- 75. Yao B, Hametner S, van Gelderen P, et al. 7 Tesla magnetic reso- rosis patients reveals pathology associated with disability. Mult nance imaging to detect cortical pathology in multiple sclerosis. PLoS One 2014;9:e108863 CrossRef Medline Scler 2021;27:2040–51 CrossRef Medline AJNR Am J Neuroradiol 43:1688–95 Dec 2022 www.ajnr.org 1695
American Journal of Neuroradiology – American Journal of Neuroradiology
Published: Dec 1, 2022
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