Title | Advanced Neuroimaging Techniques: Basic Principles and Clinical Applications |
Creator | Julius Griauzde, MD; Ashok Srinivasan, MD |
Affiliation | Department of Radiology (JG, AS), University of Michigan Health System, Ann Arbor, Michigan |
Abstract | Multiple sclerosis (MS), a demyelinating disease of the central nervous system, is multifaceted. It manifests as acute episodes as well as an accumulative chronic disability; myelin involvement as well as axonal damage; local as well as global effects; and disease load elements as well as compensatory mechanisms. The visual system, with its clear structural organization and relatively direct reflection of damage, may serve as an appropriate model to study MS. In recent years, we have witnessed a blossoming in the field of visual measures in MS. Because it is impossible to cover all different aspects of these measures, we chose to focus on several hot topics in MS literature and shed light on them through studies conducted in the visual system. We argue that numerous methods can be used to study axonal and demyelinating aspects of the disease. Although optical coherence tomography and static visual functions better reflect the axonal aspects of the disease, conduction velocity as measured by visual-evoked potential latencies and dynamic visual function mirrors myelin levels. We also posit that the classic disease load parameters cannot be the only means by which we assess a patient's condition. Novel imaging methods such as diffusion tensor imaging and functional magnetic resonance imaging can be used to assess the global effects of local damage on neighboring white matter and compensatory abilities of the brain. There have been great advances in therapeutic research in MS. However, the stratification of patients according to their prognosis and predictive outcomes in response to treatment is still in its infancy. The many facets of MS make it difficult to piece all the data together into one cohesive conclusion for the individual patient. The visual system, with our ability to assess both structure and function, offers a promising opportunity to study both pathophysiologic mechanisms and novel therapies. |
Subject | Perfusion Imaging; Functional MRI; Diffusion-Weighted Imaging; Myelin Imaging; Dual-Energy Computed Tomography; Magnetic Resonance Spectroscopy; Magnetic Resonance Fingerprinting |
OCR Text | Show State-of-the-Art Review Section Editors: Valérie Biousse, MD Steven Galetta, MD Advanced Neuroimaging Techniques: Basic Principles and Clinical Applications Julius Griauzde, MD, Ashok Srinivasan, MD Abstract: Advanced neuroimaging techniques are increasingly being implemented in clinical practice as complementary tools to conventional imaging because they can provide crucial functional information about the pathophysiology of a variety of disorders. Therefore, it is important to understand the basic principles underlying them and their role in diagnosis and management. In this review, we will primarily focus on the basic principles and clinical applications of perfusion imaging, diffusion imaging, magnetic resonance spectroscopy, functional MRI, and dual-energy computerized tomography. Our goal is to provide the reader with a basic understanding of these imaging techniques and when they should be used in clinical practice. Journal of Neuro-Ophthalmology 2018;38:101-114 doi: 10.1097/WNO.0000000000000539 © 2017 by North American Neuro-Ophthalmology Society I maging plays a crucial role in the diagnosis and management of patients with central nervous system pathologies. Advanced neuroimaging techniques allow for real-time evaluation of pathophysiology and the underlying causative microstructural processes. A basic understanding of these techniques is paramount to physicians involved in the care of patients with neurologic diseases. In this review, we address several currently available advanced neuroimaging techniques and briefly discuss newer experimental methods that may provide new molecular and functional imaging biomarkers to clinical practice in the near future. Department of Radiology (JG, AS), University of Michigan Health System, Ann Arbor, Michigan. The authors report no conflicts of interest. Address correspondence to Ashok Srinivasan, MD, Division of Neuroradiology, Department of Radiology, University of Michigan Health System, 1500 E Medical Center drive, Ann Arbor, MI 48109; E-mail: ashoks@med.umich.edu Griauzde and Srinivasan: J Neuro-Ophthalmol 2018; 38: 101-114 ADVANCED NEUROIMAGING TECHNIQUES IN CURRENT PRACTICE Perfusion Imaging Basic Concepts Perfusion imaging can be performed with both computed tomography (CT) and MRI. The goal of perfusion imaging is to noninvasively determine the perfusion characteristics of normal and abnormal tissues such as blood volume, blood flow, mean transit time (MTT), and permeability because these factors play an important role in disease detection and lesion characterization. Magnetic resonance perfusion imaging can be achieved using 3 main techniques: dynamic contrast-enhanced (DCE) imaging, dynamic susceptibility contrast (DSC) imaging, and arterial spin labelling (ASL). Dynamic Contrast-Enhanced Perfusion In DCE, T1-weighted noncontrast images initially are obtained followed by acquisition of images during the administration of a T1 shortening contrast agent such as gadolinium. By calculating signal intensity increases over time, a time-signal intensity curve can be calculated, which can then be used to derive several semiquantitative parameters, including rate of enhancement and contrast washout (1). Furthermore, DCE can be used to calculate Ktrans, which is the vascular transfer constant that describes contrast movement from blood vessels into the extracellular spaces, and has been identified as a potential marker of capillary and blood-brain barrier permeability (2). DCE is highly user dependent and requires complex acquisition techniques, which are potential drawbacks to its routine use. Dynamic Susceptibility Contrast Perfusion The principles of DSC magnetic resonance perfusion are similar to those of CT perfusion (CTP); therefore, both topics will be discussed here. DSC and CTP both use a bolus tracking technique in which a contrast agent (paramagnetic gadolinium in MRI and iodinated contrast 101 Copyright © North American Neuro-Ophthalmology Society. Unauthorized reproduction of this article is prohibited. State-of-the-Art Review in CT) is imaged during the first pass after venous injection (3). Therefore, these techniques require very rapid imaging. Although this is generally not a problem in CT, DSC requires specialized protocols to obtain several imaging slices in a very short period (hence protocols often use echo planar imaging which is fast but demonstrates more susceptibility artifacts due to their rapid gradient switching). Signal intensity (in MRI) or attenuation (in CT) then can be computed as a function of time. Using theoretical models of blood flow, these values can be correlated with contrast agent concentrations to create contrast concentration-to-time curves that track the changes in contrast concentration over the short bolus time (4). This relationship is linear in CTP, allowing for a quantitative measurement, and CTP can achieve greater spatial resolution than DSC MRI. However, CTP is associated with a relatively high dose of ionizing radiation. Once contrast concentration-to-time curves are known in the vascular spaces and brain tissue, they can be used to calculate blood flow (CBF), calculate blood volume, and MTT according to the central volume principle (product of blood flow and MTT is the blood volume) (3,5). Arterial Spin Labelling Perfusion ASL uses radiofrequency pulses to "label" the protons of flowing blood (6). Intravascular arterial blood which is flowing into the imaging slice is exposed to a continuous or intermittent pulse which saturates the magnetization of protons. By comparing these labelled slices to unlabeled (control) slices, perfusion information can be obtained (7). ASL is the only perfusion technique that does not need contrast administration and can be performed in patients with altered renal function. The downside is that ASL is not as widely available as DCE and DSC because optimization and standardization is challenging and most software packages are currently only able to CBF. Clinical Applications Traditionally, perfusion imaging was used in the evaluation of ischemic stroke to determine the extent of infarction and identify any salvageable tissue (Fig. 1) (4,8). In addition, CTP has been used in the assessment and response to treatment for cerebral vasospasm (9). The role of perfusion imaging has expanded over the past 2 decades, particularly in the areas of oncology and psychiatric illness. Perfusion imaging can be used to assess tumor vascularity, response to treatment, and recurrent disease (Fig. 2) as well as to differentiate neoplastic lesions from surrounding tissue changes (10,11). Specifically, lower grade gliomas show lower blood volume than higher grade gliomas, and recurrent brain tumors demonstrate higher blood volume compared with radiation necrosis (12,13). Early experience in head and neck tumors revealed that blood volume and Ktrans can be used to predict response to chemoradiation in patients with squamous cell carcinoma (14,15). In psychiatric disorders, ASL has shown localized abnormal perfusion in depressed and schizophrenic patients and has identified perfusion differences in responders and nonresponders to antidepressant medications (16,17). The clinical utility of ASL in psychiatric disease remains controversial and is still in its infancy. Finally, perfusion MRI has shown promise in the differentiation of various types of dementia types and in the assessment of hemodynamics in arteriovenous malformations (18-20). Functional MRI Basic Concepts Functional MRI (fMRI) is usually performed using the blood oxygen level-dependent (BOLD) imaging FIG. 1. CT perfusion parametric maps in a 21-year-old man with acute onset right hemiparesis. The CBV map (A) shows a small region of decreased blood volume in the left posterior cerebral hemisphere (arrowheads) but the CBF (B) and MTT (C) maps show a much larger region of decreased blood flow and elevated mean transit time in the left cerebral hemisphere (solid white arrows). This mismatch between the CBV and CBF/MTT maps has been thought to represent the penumbra. Deconvolution analysis of the attenuation time curves within an index artery and index vein is used to generate the different perfusion maps. CBF, calculate blood flow; CBV, calculate blood volume; CT, computed tomography; MTT, mean transit time. 102 Griauzde and Srinivasan: J Neuro-Ophthalmol 2018; 38: 101-114 Copyright © North American Neuro-Ophthalmology Society. Unauthorized reproduction of this article is prohibited. State-of-the-Art Review FIG. 2. MRI and DCE MRI in a 53-year-old male patient with proven recurrent adenoid cystic carcinoma of the right maxillary sinus. Images were obtained to evaluate for recurrence after radiation therapy. A. Axial T2 image demonstrates a heterogeneous lesion centered in the region of the right maxillary sinus and nasal cavity (arrow). B. Postcontrast axial T1 MRI shows that the lesion has an intense heterogeneous enhancement pattern (arrow). C. Perfusion map shows that the lesion has elevated Ktrans (transfer constant), that is, indicative of a lesion with higher blood flow and/or permeability, suggestive of recurrent tumor. DCE, dynamic contrast-enhanced imaging. technique. BOLD takes advantage of the differences in the intrinsic magnetic properties of oxyhemoglobin (diamagnetic) and deoxyhemoglobin (paramagnetic). Paramagnetic substances augment external magnetic fields, whereas diamagnetic substances oppose external magnetic fields, as a result of the paring of their valence electrons (21). It has been shown that oxyhemoglobin and deoxyhemoglobin in venous blood can serve as markers for overall cerebral blood flow in a particular region, which correlates with regional oxygen metabolism (22,23). The paramagnetic properties of deoxyhemoglobin cause dephasing of the protons in surrounding tissues, resulting in signal loss. Therefore, as the proportion of regional oxyhemoglobin concentrations increase and the proportion of deoxyhemoglobin decreases, regional fMRI signal will increase, identifying increases in neuronal activity. Although fMRI is a robust technique, it does suffer from several limitations. First, scan times can be long (up to 1 hour), increasing patient discomfort. In addition, adequate mapping requires active patient cooperation and participation, which can exclude claustrophobic patients and those with altered mental status. Finally, fMRI results can be highly variable between centers, making standardization of results a challenge (24). Clinical Applications Traditional applications of fMRI have been in preoperative planning before resection of dysplastic cortex and tumors FIG. 3. Trace diffusion image (A) and corresponding ADC map (B) in a 60-year-old man with infective endocarditis reveals a lesion (arrows) in the right paramedian frontal lobe with high signal on diffusion trace, low ADC, and surrounding edema. This was a pyogenic brain abscess that was drained surgically. ADC, apparent diffusion coefficient. Griauzde and Srinivasan: J Neuro-Ophthalmol 2018; 38: 101-114 103 Copyright © North American Neuro-Ophthalmology Society. Unauthorized reproduction of this article is prohibited. State-of-the-Art Review FIG. 4. Trace diffusion image (A) and the corresponding ADC map (B) in a 30-year-old man with a history of intravenous substance abuse demonstrate a small area of high signal on diffusion trace and low ADC within the right globe (arrows). This was an ocular abscess. Note the distortion of the normal shape of the globes on the trace image which occurs due to eddy currents that are induced during rapid switching of strong gradient pulses. ADC, apparent diffusion coefficient. (25). It has proved to be highly successful in determining language laterality and mapping speech areas, as well as in mapping motor and sensory cortex (see Fig. 8 of subsequent section) (26,27). More recently, fMRI has been used in the identification and classification of disorders of cognition (such as Alzheimer disease), memory, and psychiatric illnesses as well as in monitoring responses to treatment (28-30). FIG. 5. Coronal T2 (A) and postcontrast axial T1 (B) images of an infant demonstrate an iso-to mildly T2 hypointense, avidly enhancing lesion in the superior right orbit. The trace diffusion image (C) and the corresponding ADC map (D) demonstrate absence of restricted diffusion (and high ADC) in this benign infantile hemangioma (arrows). Because of their hypercellularity, malignant lesions in the head and neck tend to show high signal and low ADC on diffusion trace compared with benign lesions. ADC, apparent diffusion coefficient. 104 Griauzde and Srinivasan: J Neuro-Ophthalmol 2018; 38: 101-114 Copyright © North American Neuro-Ophthalmology Society. Unauthorized reproduction of this article is prohibited. State-of-the-Art Review FIG. 6. Axial T2 image (A) in a 54-year-old man with nasal stuffiness demonstrates a T2 hypointense mass (arrow) within the nasal cavities and ethmoidal sinuses. The lesion shows restricted diffusion on the trace diffusion image (B) and low ADC (C), suggesting a hypercellular tumor (arrows). Biopsy proved this to be nasal lymphoma. Although there is overlap of low ADC values among different head and neck malignancies, some studies have suggested that lymphoma tends to have lower values than squamous cell carcinoma. ADC, apparent diffusion coefficient. Diffusion-Weighted Imaging, Diffusion Tensor Imaging, and Tractography Basic Concepts Diffusion of water molecules is nonrandom in human tissues because of cell membranes and other barriers to water motion. Although diffusion-weighted imaging (DWI) provides only a measure of magnitude of water motion (how free is water motion), diffusion tensor imaging (DTI) provides both magnitude and orientation dependence of water motion. ADC refers to the apparent diffusion coefficient derived from DWI, and is a measure of the magnitude of water motion; therefore, higher ADC implies less restricted movement than lower ADC. Because DWI images have contribution from T2 properties and diffusion properties, tissues can sometimes be bright on DWI but not show low ADC. This phenomenon is referred to as "T2 shinethrough." In recent years, it has been possible to remove the T2 contribution to DWI by a mathematical process, resulting in a new series of images called exponential ADC. Axonal cellular membranes and myelin, which are regularly oriented, act as microstructural barriers to the diffusion of water. The result of these microstructural barriers is an orientation dependence of the movement of water molecules parallel to the orientation of white matter fibers (31). This results in relatively higher freedom of water diffusion along the long axis of the axons compared with all other directions, which is referred to as anisotropy. Quantitative measurements obtained during DTI and tractography include fractional anisotropy (FA), mean diffusivity FIG. 7. A. Postcontrast axial T1-weighted FLAIR image with tractography overlay demonstrates mild medial displacement of the left optic nerve fibers (white arrowheads) by a large skull base meningioma (arrows). The left oculomotor nerve fibers can also be seen on the same image in a more lateral location (open arrowheads). B. Postcontrast coronal T1-weighted FLAIR image with tractography overlay shows the prechiasmatic left optic nerve fibers (arrowhead) along the inferomedial aspect of the meningioma (arrow). Presurgical identification of the visual pathways can be crucial in planning approaches and minimizing morbidity. FLAIR, fluid-attenuated inversion recovery. Griauzde and Srinivasan: J Neuro-Ophthalmol 2018; 38: 101-114 105 Copyright © North American Neuro-Ophthalmology Society. Unauthorized reproduction of this article is prohibited. State-of-the-Art Review FIG. 8. Axial (A) and coronal (B) FLAIR images with tractography and functional MRI overlay demonstrate the close proximity of a WHO Grade II infiltrating oligodendroglioma in the left parietal lobe (arrowhead) with the corticospinal tracts (black arrow). Broca area activation in the left hemisphere is also seen (white arrow), confirming left hemisphere language dominance. DTI and functional MRI play important roles in identifying eloquent areas and key white matter tracts in the brain to help reduce intraoperative injury and decrease postoperative morbidity. DTI, diffusion tensor imaging; FLAIR, fluid-attenuated inversion recovery. (MD), and tract volume (TV). FA describes the degree of anisotropy in any selected area and can be used to determine what the orientation dependence of water motion is in that voxel. FA is measured on a scale from zero to one with zero representing equal motion in all directions and a value of one representing motion only in one direction. Therefore, voxels with high FA are those which contain anatomical structures demonstrating a high orientation dependence of water motion. Performing DTI and calculating the orientation dependence of water motion in each voxel is the fundamental step toward creating a map of white matter tracts (tractography). In this technique, when the directionality of water motion in a voxel is determined (assumed to be the direction of the axon), processing software is able to assess the best voxel in all the adjacent anatomy that would fit best with the direction of the index voxel. By repeating this process over and over, it is then possible to map the white matter tracts within the brain (31-35). MD refers to the overall magnitude of diffusion of water in a voxel, regardless of direction, and is akin to ADC. TV is a measure of the voxels that are included in the tract and is used to assess if there are changes in tract size over time. Clinical Applications In ischemic stroke, DWI demonstrates hyperintense signal in the infarcted tissue. The causative mechanism of FIG. 9. IHMT (A) and MT (B) images. Both IHMT and MT exploit differences in T2 relaxation times of free water and macromolecules. IHMT further enhances MT properties between water and macromolecular proteins using both positive and negative off-resonance frequencies and has the potential to depict the presence of myelin in the white matter and cortex to a greater degree than the MT images. IHMT, in-homogenous magnetization transfer; MT, magnetization transfer. Image courtesy of Scott Swanson, PhD, University of Michigan. 106 Griauzde and Srinivasan: J Neuro-Ophthalmol 2018; 38: 101-114 Copyright © North American Neuro-Ophthalmology Society. Unauthorized reproduction of this article is prohibited. State-of-the-Art Review FIG. 10. DECT images performed in a 67-year-old man with a history of melanoma who is being evaluated for brain metastases. The ability to create "virtual non-contrast" images from contrast-enhanced DECT images could help lower the radiation dose as 2 separate scans: a noncontrast and contrast enhanced do not need to be acquired. A. Contrast-enhanced DECT image at 70 keV (equivalent to conventional CT image) demonstrates a hyperdense lesion in the left frontal region. It is unclear if this is an enhancing metastasis or a hemorrhagic focus. B. Iodine subtracted image (virtual noncontrast) demonstrates that the hyperdensity was secondary to an enhancing lesion, not blood products, because the hyperdensity is no longer seen. C. Iodine overlay image (which highlights contribution of iodine on the images) again demonstrates the hyperdensity, implying this is a small enhancing lesion. CT, computed tomography; DECT, dual-energy CT. restricted diffusion in ischemic stroke is mostly attributed to cytotoxic edema resulting from altered ion gradients because of failure of ion pumps within cell membranes. The altered ion gradients cause an influx of water into cells where its motion is relatively restricted compared with extracellular water (36). Apart from its critical role in the diagnosis of acute ischemic stroke, DWI has other applications in clinical practice. These include abscesses (demonstrating restricted diffusion-Figs. 3, 4) and hypercellular malignancies (demonstrating relatively restricted diffusion compared with benign tumors-Figs. 5, 6). DTI has shown promise in its ability to assess white matter injury in different pathological entities including demyelination, trauma, and ischemia (35,37). DTI with tractography also can be used in presurgical planning to help identify the proximity of tracts to surgical lesions (such as brain tumors) (38) (Figs. 7, 8). Recent studies FIG. 11. Noncontrast DECT head performed immediately after mechanical thrombectomy for left middle cerebral artery thrombus in a 55-year-old man who presented with acute stroke symptoms 4 hours after onset. A. Noncontrast CT obtained after the thrombectomy demonstrates hyperdensity in the left frontal subarachnoid space (arrow). Because iodinated intraarterial contrast was administered during the neurointerventional procedure, this is not truly a "non-contrast" CT, implying that it is not possible to determine whether the hyperdensity represents hemorrhagic transformation of the ischemic stroke or contrast staining of ischemic tissues where the blood-brain barrier is disrupted. B. Iodine subtracted image shows resolution of the left frontal hyperdensity (arrow), consistent with contrast staining rather than hemorrhagic transformation. This information is important for management and prognosis. CT, computed tomography; DECT, dual-energy CT. Griauzde and Srinivasan: J Neuro-Ophthalmol 2018; 38: 101-114 107 Copyright © North American Neuro-Ophthalmology Society. Unauthorized reproduction of this article is prohibited. State-of-the-Art Review FIG. 12. DECT images have been promising in early studies for the differentiating of benign from malignant neck pathologies as well as improving conspicuity of lesion boundaries. Contrast-enhanced DECT images demonstrate the increased conspicuity of metastatic Level II neck lymph nodes at different virtual monochromatic energies with better appreciation of contrast enhancement and lesion margins at lower keV values. A. 70 keV image used for routine clinical interpretation (which is considered equivalent to conventional non-DECT image) shows an enlarged right Level II submandibular metastatic lymph node (arrow) in a 62-year-old man with right palatine tonsillar carcinoma. B. Virtual monochromatic image at 60 keV shows better separation of the right Level II node (arrow) from the adjacent muscle. C. Virtual monochromatic image at 50 keV shows further increased conspicuity of the right Level II lymph node (arrow) and presence of a smaller necrotic left Level II lymph node (arrowhead) that could be missed on the other images as the lesion-to-background separation is not as high as the right sided lymph node. have identified that in intracranial hemorrhage, FA values are decreased in affected white matter tracts, which have been shown to correlate with functional outcomes (39,40). Myelin Imaging Basic Concepts Evaluation of myelin content is important for the diagnosis and stratification of many pathological processes. The first technique used to detect myelin indirectly was magnetization transfer (MT). MT exploits the molecular property differences between free water and macromolecules (such as myelin), which cause significant differences in their T2 relaxation times (41). Using an off-resonance pulse, immobile protons in macromolecular structures (such as large proteins in myelin) are selectively saturated creating a contrast difference (41). Furthermore, MT can provide a semiquantitative estimate of white matter content and integrity by comparing signal intensity in sequences with and without off-resonance pulses (41,42). More recently, there are reports of in-homogenous magnetization transfer (IHMT), which is highly specific for myelinated tissues. IHMT uses both positive and negative off-resonance frequencies which enhance magnetization transfer properties between water and macromolecular proteins (43,44). IHMT contrast depends on the properties of lipid and phospholipid systems, which are abundant in myelin, making IHMT preferentially increased in myelin-containing tissues (43). Technological advances, such as multi-echo T2 relaxation MRI, have shown potential for separation of water within different 108 physiologic compartments (i.e., cerebrospinal fluid, intra/ extracellular, and myelin water) based on differences in their T2 relaxation times (45). Such separation could allow for more specific and quantitative assessments of myelin-based FIG. 13. Normal magnetic resonance spectroscopy (MRS) in the adult brain demonstrating the location of the normal choline (Ch), creatine (Cr), and N-acetyl aspartate (NAA) peaks. Hunter angle is a line drawn connecting the peaks (blue line). If the line is at approximately at 45° and connects the peak, it suggests a normal MRS. Griauzde and Srinivasan: J Neuro-Ophthalmol 2018; 38: 101-114 Copyright © North American Neuro-Ophthalmology Society. Unauthorized reproduction of this article is prohibited. State-of-the-Art Review FIG. 14. MRS performed in a patient with a cerebral abscess. Region of interest is placed in the central portion of the lesion (white box). Spectral image demonstrates an inverted lactate peak (arrow) at the characteristic location, which is an abnormal finding suggestive of necrosis. MRS, magnetic resonance spectroscopy. pathology (10). Myelin water fraction (MWF) is a measure of the amount of water trapped within myelin fibers (46). MWF has been shown to correlate with myelin density on histological analysis (45,46), allowing MWF the potential to be a marker of myelin damage (47). Clinical Applications Although myelin imaging techniques are still in their early developmental stages, they may have broad implications in identifying and tracking the progression of white matter diseases, dementia, and aging (44,48,49). Figure 9 demonstrates the improved detection of normal myelin using the IHMT technique compared with the MT method. Dual-Energy Computed Tomography Basic Principles Modern CT imaging is performed using a polychromatic beam with the energy of the X-rays, leaving the tube spanning a large spectrum. Dual-energy CT uses 2 distinct X-ray spectra, each creating a separate beam (50). As X-rays of different energies interact with a particular substance, the degree they are attenuated varies based on the energy of the X-rays. This allows the material in question to be identified and quantified based on its specific attenuation properties at specific energies (51). This is different from the current CT method of measuring Hounsfield units alone to characterize tissues because the Hounsfield unit of a tissue will vary based on the energy of the incident beam. DECT enables the creation of CT images where different materials can be highlighted or suppressed (such as water, iodine, calcium etc.). Clinical Applications The ability of DECT to characterize and quantify a variety of substances allows for a myriad of clinical applications. Differentiation and subtraction allow for the selective removal of materials from images (52). This can be used to obtain virtual noncontrast images and to differentiate iodinated contrast from blood by subtracting iodine (Figs. 10, 11); this is helpful in the poststroke thrombolysis setting. With iodine-enhanced images and/or monochromatic images at different keV (kiloelectron volt), there is improved visualization of subtle enhancing lesions and better tumor delineation (53-55) (Fig. 12). In addition, metal subtraction improves anatomic visualization in postoperative patients and calcium subtraction improves luminal assessment in carotid atherosclerotic disease and in the FIG. 15. MRS performed in a patient with Canavan disease shows region of interest placed in an area of white matter abnormality (green box). The spectral image demonstrates a markedly elevated NAA peak (yellow circle), which violates Hunter angle. NAA is significantly raised in Canavan disease because of deficiency of the enzyme aspartoacyclase that breaks down NAA. This is one of the few leukodystrophies that has a characteristic MRS signature. Ch, choline; Cr, creatine; MRS, magnetic resonance spectroscopy; NAA, N-acetyl aspartate. Griauzde and Srinivasan: J Neuro-Ophthalmol 2018; 38: 101-114 109 Copyright © North American Neuro-Ophthalmology Society. Unauthorized reproduction of this article is prohibited. State-of-the-Art Review evaluation of vascular structures at the skull base (56,57). DECT can improve signal-to-noise ratio and image quality by creating fused images from high- and lowenergy acquisitions (58). Magnetic Resonance Spectroscopy Basic Principles Human tissues are composed of many different molecules besides water that precess at different resonant frequencies when exposed to a magnetic field. Magnetic resonance spectroscopy (MRS) is based on quantification of non- water metabolite resonances in different types of tissues and different pathological conditions. The concentration of these metabolites is several orders of magnitude smaller than water, resulting in a poor signal-to-noise ratio and predisposing to significant error (59,60). Standardization and comparison of results also is a challenge because of the wide variety of available techniques and equipment (61). Despite these limitations, MRS can be very useful when used in conjunction with other imaging sequences. Each metabolite has a characteristic chemical shift measured in parts per million (Fig. 13). The most abundant soluble metabolites in brain imaging are N-acetyl aspartate (NAA), choline (Ch), and creatine (Cr). NAA is an amino acid present in high concentrations in neuronal mitochondria and is used as a marker of brain metabolism (62). Ch is a precursor to molecules used in cellular membranes and is used as a marker of membrane turnover (63). Ch is often elevated in pathological processes characterized by high cellularity and cell turnover such as malignant neoplasms (63). Cr is used as an internal standard because its values remain largely stable in various physiological and pathological conditions. Its primary use is in calculation of ratios and relative increases of other metabolites (64). Other less abundant metabolites include myo-inositol (MI) and lactate. MI is a sugar most concentrated in glial cells and is a marker for gliosis (65). Lactate is a marker of anaer- obic metabolism which is elevated in conditions characterized by tissue hypoxia (66). Clinical Applications MRS can aid in differentiating neoplasms from nonneoplasms, discerning radiation necrosis from recurrent neoplasm, and in identifying metabolic dyscrasias. Several disease processes have characteristic chemical spectra. For example, conditions with hypoxic areas such as ischemic parenchyma, necrotic tumor, and cerebral abscess have elevated lactate (Fig. 14). NAA is a marker of normal brain metabolism, but also is increased in patients with inborn errors of metabolism, such as Canavan disease, a leukodystrophy secondary to a genetic mutation in aspartoacylase, resulting in accumulation of NAA in the brain (Fig. 15) (67). More recently, decreased NAA levels have been found in aneurysmal subarachnoid hemorrhage, suggesting that global metabolic derangement plays a role in the disease process (68). Choline is often elevated in areas with rapidly dividing cells such as high-grade malignancies (Fig. 16). MRS is, however, limited in its specificity and hence should primarily be used as a complementary technique for problem solving. WHAT THE FUTURE HOLDS FOR ADVANCED NEUROIMAGING TECHNIQUES Magnetic Resonance Fingerprinting Magnetic resonance fingerprinting (MRF) is a novel imaging technique which uses a pseudo-randomized acquisition that causes the signals from different tissues to have a unique signal evolution (69). This unique "fingerprint" is simultaneously a function of the multiple material properties under investigation. The unique signal evolutions that are obtained are then matched with a reference database. Once the best match is found, the remaining parameters can be translated and assigned to the voxel being evaluated (69). By FIG. 16. MRS performed in a patient with a heterogeneously enhancing lesion in the left cerebral hemisphere. Region of interest is placed in an enhancing portion of the lesion (white box). The spectral image demonstrates a markedly elevated Ch peak (yellow circle) with decreased NAA peak indicative of a process with high cell turnover (such as an aggressive malignancy). This was a pathologically proven glioblastoma. Ch, choline; MRS, magnetic resonance spectroscopy; NAA, N-acetyl aspartate. 110 Griauzde and Srinivasan: J Neuro-Ophthalmol 2018; 38: 101-114 Copyright © North American Neuro-Ophthalmology Society. Unauthorized reproduction of this article is prohibited. Griauzde and Srinivasan: J Neuro-Ophthalmol 2018; 38: 101-114 Technique Perfusion imaging DCE perfusion DSC perfusion Underlying Principle T1 shortening property of gadolinium (enhancement) used to calculate perfusion parameters T2* effect of gadolinium (shortening) used in a first-pass bolus method to calculate perfusion through tissues ASL perfusion Radiofrequency pulses label protons of flowing blood (hence no gadolinium required) CT perfusion Iodinated contrast used to calculate attenuation change/time during first-pass bolus Functional MRI Changes in deoxyhemoglobin (paramagnetic) to oxyhemoglobin (diamagnetic) ratio during increased blood flow to neurons used to identify brain activation Key Applications DWI Longer acquisition Acute ischemic stroke Susceptibility artifacts near skull base Tumor perfusion Acute ischemic stroke Limited commercial availability Tumor perfusion Acute ischemic stroke Difficult standardization Involves ionizing radiation Tumor perfusion Surgical planning of brain tumors [ scan times Measures both magnitude and direction of water molecular motion Restricted diffusion seen in Acute ischemic stroke Abscess Hypercellular lesions (typically malignant) Mapping of white matter tracts in brain for surgical planning Detection of white matter injury in various disorders including demyelinating disease and traumatic brain injury Does not directly measure neuronal activation and relies on blood flow changes as surrogate marker Image distortion Susceptibility artifacts Requires long acquisition times for more robust datasets Can be user dependent State-of-the-Art Review DTI/tractography Measures motion of water molecules in the tissue microenvironment using string MRI gradients Potential Limitations Tumor perfusion Classify disorders of cognition 111 Copyright © North American Neuro-Ophthalmology Society. Unauthorized reproduction of this article is prohibited. TABLE 1. Summary of the principles, key applications, and limitations of advanced imaging techniques 112 ASL, arterial spin labelling; CT, computed tomography; DCE, dynamic contrast-enhanced imaging; DSC, dynamic susceptibility contrast; DTI, diffusion tensor imaging; DWI, diffusion-weighted imaging. Results can be nonspecific Work in progress Research tool for assessing myelin content and integrity Direct measurement of myelin water and myelin phospholipids Myelin imaging Lacks standardization across institutions Metal artifact reduction Characterization of lesions with relatively specific MR spectroscopic signatures such as recurrent tumors, abscesses, and Canavan disease Measurement of nonwater metabolites that have different magnetic resonances leading to characteristic chemical shifts Beam hardening artifact reduction MR spectroscopy Postprocessed images can demonstrate higher noise Difficult standardization Requires standardization across institutions Differentiate iodine from hemorrhage in the brain Ability to perform material decomposition using X-ray beams of different energies Dual-energy CT Technique (Continued ) Underlying Principle Key Applications Potential Limitations State-of-the-Art Review identifying specific parameters (T1, T2, flip angle) of tissues, MRF provides a quantitative imaging technique which can be used to assess pathophysiology in a standardized and reproducible fashion (70-72). The unique acquisition and reconstruction methods of MRF allow for faster imaging and decreased imaging artifacts (69,71). To date, the MRF technique has shown promise in creating blood volume and oxygenation maps in the brain and in identifying different stages of white matter injury in stroke (73,74). MRF also has been used in rapid cardiac imaging and in single breath hold acquisitions in the abdomen (75,76). Phase-Contrast CT Phase-contrast CT is an experimental imaging technique which is based on the refraction (change in angular trajectory) of X-rays as they pass through materials. This is in contradistinction to traditional CT imaging, which is based solely on the absorption of X-rays by materials. Research has shown that phase-contrast CT allows for greatly enhanced soft tissue contrast when compared with traditional CT (77). A recent study showed that ex vivo phase-contrast CT can provide excellent quantification of carotid plaque components which correlate well with histological analysis (78). CONCLUSIONS Table 1 summarizes the principles, applications, and limitations of a variety of advanced imaging techniques. These techniques have made vast improvements over the past few decades and have greatly expanded the role of neuroimaging in patient care. Future goals include improved image quality, eliminating artifacts, reducing radiation dose, and decreasing imaging time. STATEMENT OF AUTHORSHIP Category 1: a. Conception and design: A. Srinivasan; b. Acquisition of data: J. Griauzde, A. Srinivasan; c. Analysis and interpretation of data: J. Griauzde, A. Srinivasan. Category 2: a. Drafting the manuscript: J. Griauzde; b. Revising it for intellectual content: A. Srinivasan. Category 3: a. Final approval of the completed manuscript: A. Srinivasan, J. Griauzde. REFERENCES 1. Brix G, Semmler W, Port R, Schad LR, Layer G, Lorenz WJ. Pharmacokinetic parameters in CNS Gd-DTPA enhanced MR imaging. J Comput Assist Tomogr. 1991;15:621-628. 2. Roberts HC, Roberts TP, Brasch RC, Dillon WP. Quantitative measurement of microvascular permeability in human brain tumors achieved using dynamic contrast-enhanced MR imaging: correlation with histologic grade. AJNR Am J Neuroradiol. 2000;21:891-899. 3. Ostergaard L. Principles of cerebral perfusion imaging by bolus tracking. J Magn Reson Imaging. 2005;22:710-717. 4. Ostergaard L, Weisskoff RM, Chesler DA, Gyldensted C, Rosen BR. High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part I: mathematical approach and statistical analysis. Magn Reson Med. 1996;36:715-725. Griauzde and Srinivasan: J Neuro-Ophthalmol 2018; 38: 101-114 Copyright © North American Neuro-Ophthalmology Society. Unauthorized reproduction of this article is prohibited. State-of-the-Art Review 5. Hoeffner EG, Case I, Jain R, Gujar SK, Shah GV, Deveikis JP, Carlos RC, Thompson BG, Harrigan MR, Mukherji SK. Cerebral perfusion CT: technique and clinical applications. Radiology. 2004;231:632-644. 6. Kwong KK, Chesler DA, Weisskoff RM, Donahue KM, Davis TL, Ostergaard L, Campbell TA, Rosen BR. MR perfusion studies with T1-weighted echo planar imaging. Magn Reson Med. 1995;34:878-887. 7. Petersen ET, Zimine I, Ho YC, Golay X. Non-invasive measurement of perfusion: a critical review of arterial spin labelling techniques. Br J Radiol. 2006;79:688-701. 8. Koenig M, Klotz E, Luka B, Venderink DJ, Spittler JF, Heuser L. Perfusion CT of the brain: diagnostic approach for early detection of ischemic stroke. Radiology. 1998;209:85-93. 9. Nabavi DG, LeBlanc LM, Baxter B, Lee DH, Fox AJ, Lownie SP, Ferguson GG, Craen RA, Gelb AW, Lee TY. Monitoring cerebral perfusion after subarachnoid hemorrhage using CT. Neuroradiology. 2001;43:7-16. 10. Law M, Young RJ, Babb JS, Peccerelli N, Chheang S, Gruber ML, Miller DC, Golfinos JG, Zagzag D, Johnson G. Gliomas: predicting time to progression or survival with cerebral blood volume measurements at dynamic susceptibility-weighted contrastenhanced perfusion MR imaging. Radiology. 2008;247:490-498. 11. Srinivasan A, Mohan S, Mukherji SK. Biologic imaging of head and neck cancer: the present and the future. AJNR Am J Neuroradiol. 2012;33:586-594. 12. Barajas RF Jr, Chang JS, Segal MR, Parsa AT, McDermott MW, Berger MS, Cha S. Differentiation of recurrent glioblastoma multiforme from radiation necrosis after external beam radiation therapy with dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging. Radiology. 2009;253:486-496. 13. Thomsen H, Steffensen E, Larsson EM. Perfusion MRI (dynamic susceptibility contrast imaging) with different measurement approaches for the evaluation of blood flow and blood volume in human gliomas. Acta Radiol. 2012;53:95-101. 14. Kim S, Loevner LA, Quon H, Kilger A, Sherman E, Weinstein G, Chalian A, Poptani H. Prediction of response to chemoradiation therapy in squamous cell carcinomas of the head and neck using dynamic contrast-enhanced MR imaging. AJNR Am J Neuroradiol. 2010;31:262-268. 15. Cao Y, Popovtzer A, Li D, Chepeha DB, Moyer JS, Prince ME, Worden F, Teknos T, Bradford C, Mukherji SK, Eisbruch A. Early prediction of outcome in advanced head-and-neck cancer based on tumor blood volume alterations during therapy: a prospective study. Int J Radiat Oncol Biol Phys. 2008;72:1287-1290. 16. Kuhn S, Gallinat J. Resting-state brain activity in schizophrenia and major depression: a quantitative meta-analysis. Schizophr Bull. 2013;39:358-365. 17. Phillips ML, Chase HW, Sheline YI, Etkin A, Almeida JR, Deckersbach T, Trivedi MH. Identifying predictors, moderators, and mediators of antidepressant response in major depressive disorder: neuroimaging approaches. Am J Psychiatry. 2015;172:124-138. 18. Chao LL, Buckley ST, Kornak J, Schuff N, Madison C, Yaffe K, Miller BL, Kramer JH, Weiner MW. ASL perfusion MRI predicts cognitive decline and conversion from MCI to dementia. Alzheimer Dis Assoc Disord. 2010;24:19-27. 19. Ludemann L, Jedrzejewski G, Heidenreich J, Han ET, Bruhn H. Perfusion imaging of cerebral arteriovenous malformations: a study comparing quantitative continuous arterial spin labeling and dynamic contrast-enhanced magnetic resonance imaging at 3 T. Magn Reson Imaging. 2011;29:1157-1164. 20. Schuff N, Matsumoto S, Kmiecik J, Studholme C, Du A, Ezekiel F, Miller BL, Kramer JH, Jagust WJ, Chui HC, Weiner MW. Cerebral blood flow in ischemic vascular dementia and Alzheimer's disease, measured by arterial spin-labeling magnetic resonance imaging. Alzheimers Dement. 2009;5:454-462. 21. Saini S, Frankel RB, Stark DD, Ferrucci JT Jr. Magnetism: a primer and review. AJR Am J Roentgenol. 1988;150:735-743. 22. Ogawa S, Lee TM, Nayak AS, Glynn P. Oxygenation-sensitive contrast in magnetic resonance image of rodent brain at high magnetic fields. Magn Reson Med. 1990;14:68-78. Griauzde and Srinivasan: J Neuro-Ophthalmol 2018; 38: 101-114 23. Kim SG, Rostrup E, Larsson HB, Ogawa S, Paulson OB. Determination of relative CMRO2 from CBF and BOLD changes: significant increase of oxygen consumption rate during visual stimulation. Magn Reson Med. 1999;41:1152-1161. 24. Wurnig MC, Rath J, Klinger N, Höllinger I, Geissler A, Fischmeister FP, Aichhorn M, Foki T, Kronbichler M, Nickel J, Siedentopf C, Staffen W, Verius M, Golaszewski S, Koppelstätter F, Knosp E, Auff E, Felber S, Seitz RJ, Beisteiner R. Variability of clinical functional MR imaging results: a multicenter study. Radiology. 2013;268:521-531. 25. Achten E, Jackson GD, Cameron JA, Abbott DF, Stella DL, Fabinyi GC. Presurgical evaluation of the motor hand area with functional MR imaging in patients with tumors and dysplastic lesions. Radiology. 1999;210:529-538. 26. Bizzi A, Blasi V, Falini A, Ferroli P, Cadioli M, Danesi U, Aquino D, Marras C, Caldiroli D, Broggi G. Presurgical functional MR imaging of language and motor functions: validation with intraoperative electrocortical mapping. Radiology. 2008;248:579-589. 27. Jack CR Jr, Thompson RM, Butts RK, Sharbrough FW, Kelly PJ, Hanson DP, Riederer SJ, Ehman RL, Hangiandreou NJ, Cascino GD. Sensory motor cortex: correlation of presurgical mapping with functional MR imaging and invasive cortical mapping. Radiology. 1994;190:85-92. 28. Chen G, Ward BD, Xie C, Li W, Wu Z, Jones JL, Franczak M, Antuono P, Li SJ. Classification of Alzheimer disease, mild cognitive impairment, and normal cognitive status with largescale network analysis based on resting-state functional MR imaging. Radiology. 2011;259:213-221. 29. Thompson DG, Kesler SR, Sudheimer K, Mehta KM, Thompson LW, Marquett RM, Holland JM, Reiser R, Rasgon N, Schatzberg A, O'Hara RM. FMRI activation during executive function predicts response to cognitive behavioral therapy in older, depressed adults. Am J Geriatr Psychiatry. 2015;23:13-22. 30. Grotegerd D, Stuhrmann A, Kugel H, Schmidt S, Redlich R, Zwanzger P, Rauch AV, Heindel W, Zwitserlood P, Arolt V, Suslow T, Dannlowski U. Amygdala excitability to subliminally presented emotional faces distinguishes unipolar and bipolar depression: an fMRI and pattern classification study. Hum Brain Mapp. 2014;35:2995-3007. 31. Mori S, Zhang J. Principles of diffusion tensor imaging and its applications to basic neuroscience research. Neuron. 2006;51:527-539. 32. Mori S, Crain BJ, Chacko VP, van Zijl PC. Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann Neurol. 1999;45:265-269. 33. Conturo TE, Lori NF, Cull TS, Akbudak E, Snyder AZ, Shimony JS, McKinstry RC, Burton H, Raichle ME. Tracking neuronal fiber pathways in the living human brain. Proc Natl Acad Sci U S A. 1999;96:10422-10427. 34. Basser PJ, Jones DK. Diffusion-tensor MRI: theory, experimental design and data analysis-a technical review. NMR Biomed. 2002;15:456-467. 35. Horsfield MA, Jones DK. Applications of diffusion-weighted and diffusion tensor MRI to white matter diseases-a review. NMR Biomed. 2002;15:570-577. 36. Schaefer PW, Grant PE, Gonzalez RG. Diffusion-weighted MR imaging of the brain. Radiology. 2000;217:331-345. 37. Kraus MF, Susmaras T, Caughlin BP, Walker CJ, Sweeney JA, Little DM. White matter integrity and cognition in chronic traumatic brain injury: a diffusion tensor imaging study. Brain. 2007;130:2508-2519. 38. Sunaert S. Presurgical planning for tumor resectioning. J Magn Reson Imaging. 2006;23:887-905. 39. Chaudhary N, Pandey AS, Gemmete JJ, Hua Y, Huang Y, Gu Y, Xi G. Diffusion tensor imaging in hemorrhagic stroke. Exp Neurol. 2015;272:88-96. 40. Virji-Babul N, Borich MR, Makan N, Moore T, Frew K, Emery CA, Boyd LA. Diffusion tensor imaging of sports-related concussion in adolescents. Pediatr Neurol. 2013;48:24-29. 41. Grossman RI, Gomori JM, Ramer KN, Lexa FJ, Schnall MD. Magnetization transfer: theory and clinical applications in neuroradiology. Radiographics. 1994;14:279-290. 113 Copyright © North American Neuro-Ophthalmology Society. Unauthorized reproduction of this article is prohibited. State-of-the-Art Review 42. Dousset V, Grossman RI, Ramer KN, Schnall MD, Young LH, Gonzalez-Scarano F, Lavi E, Cohen JA. Experimental allergic encephalomyelitis and multiple sclerosis: lesion characterization with magnetization transfer imaging. Radiology. 1992;182:483-491. 43. Swanson SD, Malyarenko DI, Fabiilli ML, Welsh RC, Nielsen JF, Srinivasan A. Molecular, dynamic, and structural origin of inhomogeneous magnetization transfer in lipid membranes. Magn Reson Med. 2017;77:1318-1328. 44. Varma G, Duhamel G, de Bazelaire C, Alsop DC. Magnetization transfer from inhomogeneously broadened lines: a potential marker for myelin. Magn Reson Med. 2015;73:614-622. 45. Laule C, Leung E, Lis DK, Traboulsee AL, Paty DW, MacKay AL, Moore GR. Myelin water imaging in multiple sclerosis: quantitative correlations with histopathology. Mult Scler. 2006;12:747-753. 46. Kozlowski P, Liu J, Yung AC, Tetzlaff W. High-resolution myelin water measurements in rat spinal cord. Magn Reson Med. 2008;59:796-802. 47. Laule C, Vavasour IM, Moore GR, Oger J, Li DK, Paty DW, MacKay AL. Water content and myelin water fraction in multiple sclerosis. A T2 relaxation study. J Neurol. 2004;251:284-293. 48. Billiet T, Vandenbulcke M, Madler B, Peeters R, Dhollander T, Zhang H, Deprez S, Van den Bergh BR, Sunaert S, Emsell L. Age-related microstructural differences quantified using myelin water imaging and advanced diffusion MRI. Neurobiol Aging. 2015;36:2107-2121. 49. Su L, Blamire AM, Watson R, He J, Aribisala B, O'Brien JT. Tissue microstructural changes in dementia with Lewy bodies revealed by quantitative MRI. J Neurol. 2015;262:165-172. 50. Johnson TR. Dual-energy CT: general principles. AJR Am J Roentgenol. 2012;199:S3-S8. 51. Brooks RA. A quantitative theory of the Hounsfield unit and its application to dual energy scanning. J Comput Assist Tomogr. 1977;1:487-493. 52. Johnson TR, Krauss B, Sedlmair M, Grasruck M, Bruder H, Morhard D, Fink C, Weckbach S, Lenhard M, Schmidt B, Flohr T, Reiser MF, Becker CR. Material differentiation by dual energy CT: initial experience. Eur Radiol. 2007;17:1510-1517. 53. Brown CL, Hartman RP, Dzyubak OP, Takahashi N, Kawashima A, McCollough CH, Bruesewitz MR, Primak AM, Fletcher JG. Dual-energy CT iodine overlay technique for characterization of renal masses as cyst or solid: a phantom feasibility study. Eur Radiol. 2009;19:1289-1295. 54. Thieme SF, Johnson TR, Lee C, McWilliams J, Becker CR, Reiser MF, Nikolaou K. Dual-energy CT for the assessment of contrast material distribution in the pulmonary parenchyma. AJR Am J Roentgenol. 2009;193:144-149. 55. Yamauchi H, Buehler M, Goodsitt MM, Keshavarzi N, Srinivasan A. Dual-energy CT-based differentiation of benign posttreatment changes from primary or recurrent malignancy of the head and neck: comparison of spectral Hounsfield units at 40 and 70 keV and iodine concentration. AJR Am J Roentgenol. 2016;206:580-587. 56. Srinivasan A, Hoeffner E, Ibrahim M, Shah GV, LaMarca F, Mukherji SK. Utility of dual-energy CT virtual keV monochromatic series for the assessment of spinal transpedicular hardware-bone interface. AJR Am J Roentgenol. 2013;201:878-883. 57. Watanabe Y, Uotani K, Nakazawa T, Higashi M, Yamada N, Hori Y, Kanzaki S, Fukuda T, Itoh T, Naito H. Dual-energy direct bone removal CT angiography for evaluation of intracranial aneurysm or stenosis: comparison with conventional digital subtraction angiography. Eur Radiol. 2009;19:1019-1024. 58. Behrendt FF, Schmidt B, Plumhans C, Keil S, Woodruff SG, Ackermann D, Mühlenbruch G, Flohr T, Günther RW, Mahnken AH. Image fusion in dual energy computed tomography: effect on contrast enhancement, signal-to-noise ratio and image quality in computed tomography angiography. Invest Radiol. 2009;44:1-6. 59. Christiansen P, Henriksen O, Stubgaard M, Gideon P, Larsson HB. In vivo quantification of brain metabolites by 1H-MRS using water as an internal standard. Magn Reson Imaging. 1993;11:107-118. 114 60. Michaelis T, Merboldt KD, Bruhn H, Hänicke W, Frahm J. Absolute concentrations of metabolites in the adult human brain in vivo: quantification of localized proton MR spectra. Radiology. 1993;187:219-227. 61. Tofts PS, Wray S. A critical assessment of methods of measuring metabolite concentrations by NMR spectroscopy. NMR Biomed. 1988;1:1-10. 62. Chakraborty G, Mekala P, Yahya D, Wu G, Ledeen RW. Intraneuronal N-acetylaspartate supplies acetyl groups for myelin lipid synthesis: evidence for myelin-associated aspartoacylase. J Neurochem. 2001;78:736-745. 63. Miller BL, Chang L, Booth R, Ernst T, Cornford M, Nikas D, McBride D, Jenden DJ. In vivo 1H MRS choline: correlation with in vitro chemistry/histology. Life Sci. 1996;58:1929-1935. 64. Provencher SW. Estimation of metabolite concentrations from localized in vivo proton NMR spectra. Magn Reson Med. 1993;30:672-679. 65. Brand A, Richter-Landsberg C, Leibfritz D. Multinuclear NMR studies on the energy metabolism of glial and neuronal cells. Dev Neurosci. 1993;15:289-298. 66. Henriksen O, Gideon P, Sperling B, Olsen TS, Jørgensen HS, Arlien-Søborg P. Cerebral lactate production and blood flow in acute stroke. J Magn Reson Imaging. 1992;2:511-517. 67. Janson CG, McPhee SW, Francis J, Shera D, Assadi M, Freese A, Hurh P, Haselgrove J, Wang DJ, Bilaniuk L, Leone P. Natural history of Canavan disease revealed by proton magnetic resonance spectroscopy (1H-MRS) and diffusion-weighted MRI. Neuropediatrics. 2006;37:209-221. 68. Wagner M, Jurcoane A, Hildebrand C, Güresir E, Vatter H, Zanella FE, Berkefeld J, Pilatus U, Hattingen E. Metabolic changes in patients with aneurysmal subarachnoid hemorrhage apart from perfusion deficits: neuronal mitochondrial injury? AJNR Am J Neuroradiol. 2013;34:1535- 1541. 69. Ma D, Gulani V, Seiberlich N, Liu K, Sunshine JL, Duerk JL, Griswold MA. Magnetic resonance fingerprinting. Nature. 2013;495:187-192. 70. Gao Y, Chen Y, Ma D, Jiang Y, Herrmann KA, Vincent JA, Dell KM, Drumm ML, Brady-Kalnay SM, Griswold MA, Flask CA, Lu L. Preclinical MR fingerprinting (MRF) at 7 T: effective quantitative imaging for rodent disease models. NMR Biomed. 2015;28:384-394. 71. Jiang Y, Ma D, Seiberlich N, Gulani V, Griswold MA. MR fingerprinting using fast imaging with steady state precession (FISP) with spiral readout. Magn Reson Med. 2015;74:1621- 1631. 72. Warntjes JB, Leinhard OD, West J, Lundberg P. Rapid magnetic resonance quantification on the brain: optimization for clinical usage. Magn Reson Med. 2008;60:320-329. 73. Christen T, Pannetier NA, Ni WW, Qiu D, Moseley ME, Schuff N, Zaharchuk G. MR vascular fingerprinting: a new approach to compute cerebral blood volume, mean vessel radius, and oxygenation maps in the human brain. Neuroimage. 2014;89:262-270. 74. Ma HT, Ye C, Wu J, Yang P, Chen X, Yang Z, Ma J. A preliminary study of DTI Fingerprinting on stroke analysis. Conf Proc IEEE Eng Med Biol Soc. 2014;2014:2380-2383. 75. Chen Y, Jiang Y, Pahwa S, Ma D, Lu L, Twieg MD, Wright KL, Seiberlich N, Griswold MA, Gulani V. MR fingerprinting for rapid quantitative abdominal imaging. Radiology. 2016;279:278-286. 76. Hamilton JI, Jiang Y, Chen Y, Ma D, Lo WC, Griswold M, Seiberlich N. MR fingerprinting for rapid quantification of myocardial T1, T2, and proton spin density. Magn Reson Med. 2017;77:1446-1458. 77. Donath T, Pfeiffer F, Bunk O, Grünzweig C, Hempel E, Popescu S, Vock P, David C. Toward clinical X-ray phase-contrast CT: demonstration of enhanced soft-tissue contrast in human specimen. Invest Radiol. 2010;45:445-452. 78. Hetterich H, Willner M, Fill S, Herzen J, Bamberg F, Hipp A, Schüller U, Adam-Neumair S, Wirth S, Reiser M, Pfeiffer F, Saam T, Phase-contrast CT. Qualitative and quantitative evaluation of atherosclerotic carotid artery plaque. Radiology. 2014;271:870-878. Griauzde and Srinivasan: J Neuro-Ophthalmol 2018; 38: 101-114 Copyright © North American Neuro-Ophthalmology Society. Unauthorized reproduction of this article is prohibited. |
Date | 2018-03 |
Language | eng |
Format | application/pdf |
Type | Text |
Publication Type | Journal Article |
Source | Journal of Neuro-Ophthalmology, December 2018, Volume 38, Issue 1 |
Publisher | Lippincott, Williams & Wilkins |
Holding Institution | Spencer S. Eccles Health Sciences Library, University of Utah, 10 N 1900 E SLC, UT 84112-5890 |
Rights Management | © North American Neuro-Ophthalmology Society |
ARK | ark:/87278/s69h010w |
Setname | ehsl_novel_jno |
ID | 1404043 |
Reference URL | https://collections.lib.utah.edu/ark:/87278/s69h010w |