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| ORIGINAL ARTICLE |
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| Year : 2013 | Volume
: 61
| Issue : 1 | Page : 26-34 |
Using susceptibility-weighted images to quantify iron deposition differences in amnestic mild cognitive impairment and Alzheimer's disease
Dan Wang1, Dan Zhu2, Xiao-Er Wei1, Yue-Hua Li1, Wen-Bin Li1
1 Department of Diagnostic and Interventional Radiology, The Sixth Affiliated People's Hospital, Shanghai Jiao Tong University, Shanghai, China
2 Department of Neurology, Second Affiliated Hospital of Harbin Medical University, China
| Date of Submission | 04-Nov-2012 |
| Date of Decision | 17-Nov-2012 |
| Date of Acceptance | 20-Jan-2013 |
| Date of Web Publication | 4-Mar-2013 |
Correspondence Address:
Wen-Bin Li
Institute of Diagnostic and Interventional Radiology, The Sixth Affiliated People's Hospital, Shanghai Jiao Tong University, No. 600, Yi Shan Road, Shanghai 200233
China
 Source of Support: National Natural Science Foundation of China
NO 81271540, Conflict of Interest: None  | Check |
DOI: 10.4103/0028-3886.107924
Aim: To quantify iron deposition in Alzheimer's disease (AD), amnestic mild cognitive impairment (aMCI), and control individuals using susceptibility weighted imaging (SWI). Materials and Methods: Sixty participants (22 aMCI, 20 AD, 18 normal controls) underwent conventional magnetic resonance imaging (MRI) and SWI using axial/oblique coronal sequences. Phase images were used to calculate bilateral iron deposition in 18 regions of interest (ROI). The radian angle value was calculated and compared between the three participant groups. Results: The difference in radian angle value was significant between the aMCI and control groups in the left (L)-hippocampus, L-head of the caudate nucleus, R-lenticular nucleus, L-lenticular nucleus (P =0.02239, <0. 001, 0.03571, 0.00943, respectively). The difference in radian angle value was significant between the AD and aMCI groups in the R-cerebellar hemisphere, L-cerebellar hemisphere, R-hippocampus, L-hippocampus, R-red nucleus, R-thalamus, L-thalamus, and splenium of corpus callosum (P =0.02754, 0.01839, 0.00934, 0.04316, 0.02472, 0.00152, <0.001, 0.01448, respectively). Pearson correlation coefficients of the Mini-Mental State Examination score were all significant for the bilateral cerebellar hemisphere, hippocampus, red nucleus, lenticular nucleus, thalamus, R-head of the caudate nucleus, and splenium of corpus callosum. Conclusion: Iron deposition in the hippocampus, head of the caudate nucleuslenticular nucleus, and thalamus are significantly different between individuals with aMCI, AD, and controls. The thalamus is a particularly sensitive area. Using SWI to quantify the iron deposition is a useful tool in detecting aMCI and AD.
Keywords: Alzheimer′s disease, magnetic resonance imaging, quantitative iron deposition, susceptibility-weighted imaging
How to cite this article:
Wang D, Zhu D, Wei XE, Li YH, Li WB. Using susceptibility-weighted images to quantify iron deposition differences in amnestic mild cognitive impairment and Alzheimer's disease. Neurol India 2013;61:26-34 |
How to cite this URL:
Wang D, Zhu D, Wei XE, Li YH, Li WB. Using susceptibility-weighted images to quantify iron deposition differences in amnestic mild cognitive impairment and Alzheimer's disease. Neurol India [serial online] 2013 [cited 2023 Jun 3];61:26-34. Available from: https://www.neurologyindia.com/text.asp?2013/61/1/26/107924 |
| » Introduction | |  |
Alzheimer's disease (AD) is a chronic progressive neurodegenerative disease that is mainly found in older people, which has dementia as a prominent symptom. The disease often begins with memory impairment and later develops into increasingly comprehensive decline of cognitive function. Amnestic mild cognitive impairment (aMCI) includes defective memory or cognition without the presence of dementia. It is estimated that 12-15% of individuals with aMCI may progress to AD each year. It is worth noting that aMCI has a relatively reversible character, and it has been reported that cognitive function in about 20% to 25% of individuals with aMCI is restored to normal levels. Thus, early detection of aMCI and appropriate timely intervention is of great clinical significance. [1]
An increasing number of studies show that the excessive brain iron deposition in individuals with AD is closely related to the development and progression of AD. Iron metabolism disorder has been found at autopsy in several parts of the brains in individuals with AD, where brain tissue of higher iron content has been shown to be identical to the involved lesion. [2] Iron ions in brain tissue can catalyze free radicals and promote lipid peroxidation, playing an important role in the action of oxidative stress injury. The presence of senile plaques is one of the three AD pathological changes, and its main component is the β-amyloid protein, which plays a leading role in the occurrence and development of AD. [2]
The current rapid development of magnetic resonance imaging (MRI) technology has meant that an increasing number of imaging techniques is used in the diagnosis of aMCI and AD. These techniques include diffusion tensor imaging (DTI), magnetic resonance spectroscopy (MRS) and single-photon emission computed tomography (SPECT) among others. DTI is used to observe changes in white matter fibers and MRS and SPECT are used to detect reduction in levels of brain glucose metabolism, neuronal damage, and other anomalies in the frontal lobe, temporal lobe, cingulate gyrus, parahippocampal gyrus, and other locations. [3],[4] However, as an important method of assessing iron deposition in AD, susceptibility-weighted imaging (SWI) can give a clear image of the cortical structure of the brain. [5] This is helpful for detection in the early stages of the disease. However, SWI is rarely used to quantify changes in iron deposition in the brain.
SWI represents a new method for enhancing contrast in MRI. Conventional imaging relies on the magnitude information to generate the images; phase information has been discarded except for a few applications in flow imaging. [6] However, the phase images and the magnitude images can be combined to create susceptibility-weighted images. In addition, phase imaging can be used to quantify iron accumulation. [5],[7],[8] The aim of this study was to use radian angle values to determine differences in iron deposition between aMCI, AD, and control individuals.
| » Materials and Methods | | |
Participants
Sixty participants (22 with aMCI, 20 with AD, and 18 normal controls) were enrolled in the study: 28 male, 32 female [Table 1]. The inclusion criteria required all participants to be right-handed. They were examined clinically by both neurologists and psychologists and the criteria were also checked by interview with them or their relatives. Exclusion criteria included structural abnormalities that could produce dementia, such as cortical infarction, tumors, subdural hematoma, brain trauma, epilepsy, alcoholism, psychiatric illness, or other systemic diseases that affect brain function. Participants taking certain psychoactive medications were excluded. Control participants scored 24-30 on the Mini-Mental State Examination (MMSE) [9],[10] had a clinical dementia rating (CDR) of 0, [11] and no depression as measured by the Geriatric Depression Scale. [10],[12] Participants with aMCI fulfilled the criteria set by Petersen et al., (i.e. MMSE score above 23, CDR of 0.5 on subjective memory complaints, delayed verbal recall at least 1.5 standard deviations below the respective age standard, normal general cognitive function, and capability to perform normal activities of daily living). [13] Participants with AD had an MMSE score at baseline of 20-26, CDR of 0.5 or 1.0, and met the National Institute of Neurological and Communicative Diseases and Stroke/Alzheimer's Disease and Related Disorders Association guidelines for probable AD. [14] | Table 1: The comparison of group differences on age, gender, and years of education of the normal control group (group 1), MCI group (group 2), and alzheimer's disease group (group 3)
Click here to view |
We obtained written informed consent both from participants themselves and their guardians for the use of data prior to MRI examination. This study was reviewed and approved by the Ethics Review Board of the Shanghai 6 th People's Hospital affiliated with Shanghai Jiao Tong University, Shanghai, People's Republic of China.
Magnetic resonance examination and measurement
A German Siemens Verio 3.0 T MR with a Head 32-channel was used. The imaging sequences included conventional MR sequences T1W, T2W, DWI, and SWI. Scanning parameters were as follows: T2W: TR/TE 6,000 ms/95 ms, base resolution 384 × 384, slice thickness 6 mm, Distance factor 30% (Dist. factor), average 1, Field of View (FOV) 250 mm; T1W Fluid Attenuated Inversion Recovery: Axial and sagittal scanning TR/TE 2000 ms/9 ms, base resolution 320 × 320, slice thickness 6 mm, Dist. factor 30%, average 1, FOV 250 mm. SWI: Axial scanning TR/TE 28 ms/20 ms, base resolution 320 × 320, slice thickness 1.2 mm, Dist. factor 30%, average 1, FOV 230 mm. Oblique coronal scan parallel to the brainstem was taken for visualization of the hippocampus area, TR/TE 28 ms/20 ms, base resolution 320 × 320, slice thickness 2 mm, Dist. factor 30%, average 1, FOV 230 mm. SWI post-processing software generated corrected phase diagrams, magnetic moment and SWI Maximum/Minimum Intensity Projection (MIP) images. [8]
Image analysis
We used the phase images to quantify iron deposition. Using the German Siemens Workstation, two neuro-radiologists (with 5 years and 8-years experience, respectively) manually measured cerebellar hemisphere, hippocampus, substantia nigra, red nucleus, head of caudate nucleus, lenticular nucleus, thalamus, genu of corpus callosum, and splenium of corpus callosum as regions of interest (ROI) [Figure 1]. Following this, the mean phase values were measured by the following conversion formula. | Figure 1: (A-E) Axial phase and Coronal phase image images showing ROIs: (a) Cerebellar hemisphere (b) hippocampus (c) substantia nigra (d) red nucleus (e) head of caudate nucleus (f) lenticular nucleus (g) thalamus (h) genu of corpus callosum (i) splenium of corpus callosum
Click here to view |
As the measured value (Y) has a range of (-4096~4095), which maps to the phase value X (Pi ~ -Pi) (MAGNETOM, Verio, Software, VB17), so the conversion formula was presented as follows:
Y=-X*π/4096
Where X =regions of interest value and Y =radian angle value. The values thus obtained were for the head of the caudate nucleus and the lenticular nucleus. Following measurement, the mean values were calculated for statistical analysis. The measurement method can be seen in [Figure 1]. The analysis of variance (ANOVA) model was used to compare values between the different groups using the Fish-Least significant difference (LSD) test (two sample t-test comparison), with an overall statistical significance level of 0.05. For the correlation analysis, Pearson correlation coefficient was performed to analyze the relationship between changes in iron deposition in all the ROIs and MMSE score. A two-tailed test of significance is used. Correlation was significant at the 0.05 level.
| » Results | | |
The mean radian angle value of the ROIs of the brain, which were measured, is shown in [Figure 2]a-p, and [Figure 3] and [Table 2]. Iron deposition compared between the aMCI, AD, and control groups were as follows: In the comparison between the L-hippocampus, L-head of the caudate nucleus, R-lenticular nucleus, L-lenticular nucleus radian angle value, P values =0.02239, <0. 001, 0.03571, 0.00943, respectively, indicating that there was a significant difference between the control and aMCI group. | Figure 3: Mean radian angle values at all locations in control group (Group 1), aMCI group (Group 2), and AD group (Group 3)
Click here to view |
 | Table 2: Details of all locations' angle radian value of normal control group (group 1), MCI group (group 2), and Alzheimer's disease group (group 3)
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In the comparison between the R-cerebellar hemisphere, L-cerebellar hemisphere, R-hippocampus, L-hippocampus, R-red nucleus, R-thalamus, L-thalamus and splenium of corpus callosum radian angle value, P values =0.02754, 0.01839, 0.00934, 0.04316, 0.02472, 0.00152, <0.001, 0.01448, showing significant difference between the aMCI and AD groups.
In the comparison of the R-cerebellar hemisphere, L-cerebellar hemisphere, R-hippocampus, L-hippocampus, R-red nucleus, L-head of the caudate nucleus, R-lenticular nucleus, L-lenticular nucleus, R-thalamus, L-thalamus, and splenium of the corpus callosum radian angle value, P values =0.00299, 0.00108, 0.00193, <0.001, 0.01744, 0.02339, <0.001, 0.00714, <0.001, 0.00278, 0.02878) showing there was significant difference between the control and AD group.
Pearson correlation coefficients of the MMSE score and all the regions were as follows [Table 3], [Figure 4]: Regions whose correlation with MMSE score was significant-the R-cerebellar hemisphere, L-cerebellar hemisphere, R-hippocampus, L-hippocampus, R-red nucleus, L-red nucleus, R-head of caudate nucleus, R-lenticular nucleus, L-lenticular nucleus, R-thalamus, L-thalamus, and splenium of corpus callosum, where coefficients were 0.36999, 0.3783, 0.40081, 0.40741, 0.2892, 0.2599, 0.2593, 0.40462, 0.26039, 0.54453, 0.46979, -0.28888 (P values =0.00362, 0.00288, 0.00151, 0.00123, 0.02501, 0.04492, 0.04543, 0.00134, 0.0445, <0.001, <0.001,0.02519, respectively) [Table 3], [Figure 4]. | Figure 4: Pearson correlation coefficient performed to analyze the relationship between changes in iron deposition in all the ROIs and MMSE score. The correlations were all significant for the bilateral cerebellar hemisphere, hippocampus, red nucleus, lenticular nucleus, thalamus, R-head of the caudate nucleus, and splenium of corpus callosum
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 | Table 3: Pearson correlation coefficients of the mini-mental state examination score and angle radian values of all the regions
Click here to view |
| » Discussion | | |
This study found that the ROIs with the significant differences in iron deposition between the three groups of participants were the R-hippocampus/L-hippocampus, L-head of the caudate nucleus, L-lenticular nucleus and the R-thalamus, and L-thalamus. These areas also have a significant correlation with the MMSE score.
It is observed that iron deposition increased in the hippocampal area. We interpret this as: In individuals with aMCI, pathological changes include neurofibrillary tangles and neocortical senile plaques as well as a decrease in the number of neurons in the hippocampus which can be seen in early AD. All of the factors mentioned above can cause abnormal blood perfusion in the hippocampus and surrounding structure, these factors can get reduced during glucose metabolism and make neuronal damage. Although these pathological changes and iron deposition in the brain are not directly related, unique changes are taking place in blood products and in the vascular structure in the brain (blood products include deoxyhemoglobin, methemoglobin, and hemosiderin). [15] This result is consistent with previous structural and metabolic studies. Juottonen et al., Li et al. and Karas et al. found the atrophy hippocampus is a sensitive area for detecting AD. [16-18] Kantarci et al. consider that hippocampal and parahippocampal diffusivity were complementary to structural MRI in discriminating AD from control individuals. [19] This study and these findings are consistent with the study results of resting-state functional MRI from Gang Chen's team, who found that the bilateral temporal lobe hippocampus of aMCI and AD individuals had a significantly abnormal activation region. [1]
In terms of the iron deposition changes in the L-head of the caudate nucleus, the L-lenticular nucleus and R-thalamus, and the L-thalamus of the basal ganglia, SWI was very sensitive to detect the changes between normal control and aMCI and AD groups. We try to explain: These nuclei are composed of a series of brain deep nuclei which form a functional unit. They are connected with the cerebral cortex and brainstem. They not only control voluntary movement, but are also involved in higher cognitive functions such as memory, emotion, and learning. Lesions of the basal ganglia can lead to a variety of cognitive impairments. [2],[19],[20] A different view came from Stahl et al., they concluded that DTI can be used to confirm clinical manifestation of AD but is less applicable in the detection of aMCI. [3] Their ROIs comprised of white matter only, no nucleus of the basal ganglia were included. This may explain to a certain extent why DTI is less sensitive in detecting aMCI.
The areas of the L-hippocampus, L-head of the caudate nucleus, and L-lenticular nucleus were very sensitive to iron deposition changes between the control and aMCI groups in our study. All these areas lay on the left side of brain. We suggest that this phenomenon may be associated with a dominant left hemisphere. In the studies by Haacke et al., iron deposition in the globus pallidus on the right side is less than on the left side. [8],[21]
According to the results of this study, the iron deposition in the thalamus is lower than head of the caudate nucleus or lenticular nucleus [Figure 2] and [Figure 3]. But thalamus had higher sensitivity and it also had the highest correlation with the MMSE [Figure 4]. This result is consistent with the new understanding of the function of the thalamus. In recent years, the thalamus has come to be considered as the core organ which generates consciousness and awareness. [22-24] This explains why the thalamus has higher sensitivity for detecting cognitive impairment, neuronal loss, and abnormal iron deposition such as that which occurs in aMCI and AD.
This study differed from previous studies in some respects. Unlike the DTI and MRS used in the past, this study applied SWI phase imaging to quantify iron deposition, which was a more sensitive marker for iron deposition in the nucleus. In a previous study, Gang Chen used Resting-State Functional MRI to detect abnormal activation of the cerebral cortex in aMCI and AD. FDRI (field-dependent R2 increase) was also used for iron deposition in some studies. [21] But FDRI measurement results changed with the external magnetic field strength, accurate FDRI results require the same brain tissue to be tested under the different field strength MR. So FDRI is very difficult to be widely used and is very difficult to get the accurate iron deposition results. [20] In contrast, our study focused on abnormal changes in the brain nuclei. [1] In this study, in addition to conventional axial scanning, an oblique coronal scan was used for a clear view of the hippocampus. As the hippocampal structure is unclear, the parahippocampal gyrus has often been used as the ROI. So, in our study, the sensitivity for the detection of hippocampal lesions was improved. [18]
There is a limitation to our study. Although the ROIs in this study include the corpus callosum, white fiber iron deposition is very small in size, reducing the sensitivity in terms of detecting changes in white matter fibers compared with DTI and MRS. [25-27] Further studies, which examine the relationship between nuclei and their white matter fiber regions, should be done in the future to improve the detection rate of aMCI and AD.
| » References | | |
| 1. | Chen G, Ward BD, Xie C, Li W, Wu Z, Jones JL, et al. Classification of Alzheimer disease, mild cognitive impairment, and normal cognitive status with large-scale network analysis based on resting-state functional MR imaging. Radiology 2011;259:213-21. 
[PUBMED] |
| 2. | Kantarci K, Knopman DS, Dickson DW, Parisi JE, Whitwell JL, Weigand SD, et al. Alzheimer disease: Postmortem neuropathologic correlates of antemortem 1H MR spectroscopy metabolite measurements. Radiology 2008;248:210-20.
[PUBMED] |
| 3. | Stahl R, Dietrich O, Teipel SJ, Hampel H, Reiser MF, Schoenberg SO. White matter damage in Alzheimer disease and mild cognitive impairment: Assessment with diffusion-tensor MR imaging and parallel imaging techniques. Radiology 2007;243:483-92.
[PUBMED] |
| 4. | Yoshiura T, Mihara F, Kuwabara Y, Ogomori K, Kaneko K, Tanaka A, et al. MR relative cerebral blood flow mapping of Alzheimer disease: Correlation with Tc-99m HMPAO SPECT. Acad Radiol 2002;9:1383-7.
[PUBMED] |
| 5. | Zhu WZ, Zhong WD, Wang W, Zhan CJ, Wang CY, Qi JP, et al. Quantitative MR phase-corrected imaging to investigate increased brain iron deposition of patients with Alzheimer disease. Radiology 2009;253:497-504.
[PUBMED] |
| 6. | Siegelman ES, Mitchell DG, Semelka RC. Abdominal iron deposition: Metabolism, MR findings, and clinical importance. Radiology 1996;199:13-22.
[PUBMED] |
| 7. | De Rochefort L, Liu T, Kressler B, Liu J, Spincemaille P, Lebon V, et al. Quantitative susceptibility map reconstruction from MR phase data using bayesian regularization: Validation and application to brain imaging. Magn Reson Med 2010;63:194-206.
[PUBMED] |
| 8. | Haacke EM, Ayaz M, Khan A, Manova ES, Krishnamurthy B, Gollapalli L, et al. Establishing a baseline phase behavior in magnetic resonance imaging to determine normal vs. abnormal iron content in the brain. J Magn Reson Imaging 2007;26:256-64.
[PUBMED] |
| 9. | Folstein MF, Folstein SE, McHugh PR. "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975;12:189-98.
[PUBMED] |
| 10. | Gross AL, Manly JJ, Pa J, Johnson JK, Park LQ, Mitchell MB, et al. Cortical signatures of cognition and their relationship to Alzheimer's disease. Brain Imaging Behav 2012;6:584-98.
[PUBMED] |
| 11. | Morris JC. The Clinical Dementia Rating (CDR): Current version and scoring rules. Neurology 1993;43:2412-4.
[PUBMED] |
| 12. | Brink TL, Yesavage JA. Somatoform disorders: Differentiation of conversion, hypochondriacal, psychophysiologic, and related disorders. Postgrad Med 1982;72:189-94,96,98.
[PUBMED] |
| 13. | Petersen RC, Doody R, Kurz A, Mohs RC, Morris JC, Rabins PV, et al. Current concepts in mild cognitive impairment. Arch Neurol 2001;58:1985-92.
[PUBMED] |
| 14. | McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer's disease: Report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease. Neurology 1984;34:939-44.
[PUBMED] |
| 15. | Walhovd KB, Fjell AM, Brewer J, McEvoy LK, Fennema-Notestine C, Hagler DJ Jr, et al. Combining MR imaging, positron-emission tomography, and CSF biomarkers in the diagnosis and prognosis of Alzheimer disease. AJNR Am J Neuroradiol 2010;31:347-54.
|
| 16. | Juottonen K, Laakso MP, Partanen K, Soininen H. Comparative MR analysis of the entorhinal cortex and hippocampus in diagnosing Alzheimer disease. AJNR Am J Neuroradiol 1999;20:139-44.
[PUBMED] |
| 17. | Karas G, Sluimer J, Goekoop R, van der Flier W, Rombouts SA, Vrenken H, et al. Amnestic mild cognitive impairment: Structural MR imaging findings predictive of conversion to Alzheimer disease. AJNR Am J Neuroradiol 2008;29:944-9.
[PUBMED] |
| 18. | Li Y, Li J, Segal S, Wegiel J, De Santi S, Zhan J, et al. Hippocampal cerebrospinal fluid spaces on MR imaging: Relationship to aging and Alzheimer disease. AJNR Am J Neuroradiol 2006;27:912-8.
|
| 19. | Kantarci K, Avula R, Senjem ML, Samikoglu AR, Zhang B, Weigand SD, et al. Dementia with Lewy bodies and Alzheimer disease: Neurodegenerative patterns characterized by DTI. Neurology 2010;74:1814-21.
[PUBMED] |
| 20. | Pfefferbaum A, Adalsteinsson E, Rohlfing T, Sullivan EV. MRI estimates of brain iron concentration in normal aging: Comparison of field-dependent (FDRI) and phase (SWI) methods. Neuroimage 2009;47:493-500.
[PUBMED] |
| 21. | Klöppel S, van Eimeren T, Glauche V, Vongerichten A, Münchau A, Frackowiak RS, et al. The effect of handedness on cortical motor activation during simple bilateral movements. Neuroimage 2007;34:274-80.
|
| 22. | Ohara S, Lenz FA. Reorganization of somatic sensory function in the human thalamus after stroke. Ann Neurol 2001;50:800-3.
|
| 23. | Rada P, Hernandez L, Hoebel BG. Feeding and systemic D-amphetamine increase extracellular acetylcholine in the medial thalamus: A possible reward enabling function. Neurosci Lett 2007;416:184-7.
|
| 24. | Salt TE, Eaton SA. Function of non-NMDA receptors and NMDA receptors in synaptic responses to natural somatosensory stimulation in the ventrobasal thalamus. Exp Brain Res 1989;77:646-52.
|
| 25. | Schenck JF, Zimmerman EA, Li Z, Adak S, Saha A, Tandon R, et al. High-field magnetic resonance imaging of brain iron in Alzheimer disease. Top Magn Reson Imaging 2006;17:41-50.
|
| 26. | Constans JM, Meyerhoff DJ, Gerson J, MacKay S, Norman D, Fein G, et al. H-1 MR spectroscopic imaging of white matter signal hyperintensities: Alzheimer disease and ischemic vascular dementia. Radiology 1995;197:517-23.
|
| 27. | Schuff N, Amend DL, Meyerhoff DJ, Tanabe JL, Norman D, Fein G, et al. Alzheimer disease: Quantitative H-1 MR spectroscopic imaging of frontoparietal brain. Radiology 1998;207:91-102.
|
[Figure 1], [Figure 2], [Figure 3], [Figure 4]
[Table 1], [Table 2], [Table 3]
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