The acquisition of T1- or T2-weighted images, and interpretation of the associated image information and tissue contrast, is familiar to most clinicians and imaging scientists. However, the underlying biophysical processes that ultimately give rise to this contrast, and their relationships to tissue biochemistry and microstructure, remain poorly understood. Further, while qualitative T1- and T2-weighted imaging is pervasive in the clinical and research domains, quantitative mapping of these intrinsic relaxation parameters remains nascent despite many potential advantages.
In conventional qualitative T1- and T2-weighted imaging, tissue contrast is created by adjusting the sensitivity of the acquired signal to differences in the tissue relaxation times. Variation in signal sensitivity is commonly achieved through the choice of pulse sequence and manipulation of acquisition parameters (eg, flip angle, echo time, repetition time, inversion time; Fig. 1). However, while the resultant signal may be preferentially weighted toward T1, T2, or proton spin density (ρ) differences, the signal contrast depends on a mixture of ρ, T2, and T2*, as well as extraneous factors. These factors include acquisition parameters, receiver coil geometry, and sensitivity and signal amplifier gains (Fig. 2). This nonlinear blend of signal sources, coupled with inconsistent hardware corruption, renders the physical interpretation of signal contrast or intensities changes challenging, Further, it precludes any direct comparisons of intensity values across subjects, time points, or imaging centers.
Interpretation of imaging data may be simplified by separating these independent sources through direct calculation of the proton density and spin relaxation times. The acquisition of ρ, T1, or T2 "maps," such as those shown in Figure 3, can facilitate improved characterization of tissue, enhance image tissue contrast, and provide a more direct link between the observed signal changes and the microanatomical alterations distinguished via histochemistry and histology. Further, the quantitative nature of the data allows ready comparison across longitudinal time points and against population-derived norms, as well as permits more meaningful interpretation of intensity changes. In this review, we will highlight these potential advantages after a brief review of the biophysical basis of relaxation. Methods for map acquisition and analysis will be examined, and a summary of significant neuroimaging results will be presented.
Before delving into this work, a word first about notation and terminology. Throughout this text, we use the term relaxometry to refer to the quantitative measurement of relaxation times, as has become the common convention. However, before the advent of imaging, and in other applications of magnetic resonance, relaxometry referred to the study of the processes and mechanisms that give rise to the relaxation phenomena.
BIOPHYSICAL BASIS OF RELAXATION
When a collection of proton spins are placed in a strong external magnetic field, B0, the individual spins align either parallel or antiparallel to the field. At equilibrium, slightly more protons align in the parallel orientation, resulting in a small net magnetic vector. If tilted away from the direction of B0, this vector precesses about the external field at the Larmor frequency, ⌉0, equal to B0 multiplied by the proton gyromagnetic ratio (∼42.59 MHz/T for proton spins). In practice, a radiofrequency (RF) pulse applied at ⌉0 tilts the magnetic vector away from B0 and into the transverse plane and causes the individual spins to align in orientation (become phase coherent). When the RF pulse is removed, the magnetization recovers back to equilibrium, with the individual spins returning to their original parallel or antiparallel direction and dephasing in the transverse plane. T1 processes govern the regrowth of the longitudinal magnetization, while T2 processes describe the loss of phase coherence in the transverse magnetization (Fig. 4).
Intrinsically, relaxation is a process of molecular motion, interaction, and energy exchange.1 In the case of proton (1H) MRI, T1 relaxation involves an exchange of energy between water protons and the surrounding lipids, proteins, and macromolecules (collectively referred to as the "lattice"). Hence, T1 is also referred to as the spin-lattice relaxation time. T2 relaxation, in contrast, refers to the dephasing of the water protons due to interactions between them. As individual spins move within an ensemble, they experience small variations in their local magnetic fields due to each other's presence. These small variations cause the collection to slowly dephase. Accordingly, T2 is termed the spin-spin relaxation time. In addition to spin-spin interactions, dephasing can also be induced by macroscopic inhomogeneities in the applied magnetic field. This macroscopic dephasing is termed T2′, and the combined effect of T2 and T2′ is referred to as T2* (where 1/T2* = 1/T2 + 1/T2′).
As both T1 and T2 arise from molecular motion and proton-proton interactions, they are directly influenced by the local biophysical structure and biochemical environment. In particular, among other microstructural characteristics T1 and T2 depend on the local tissue density (ie, water content and mobility), macromolecule, protein and lipid composition, and paramagnetic atom (eg, iron) concentration. Thus, changes in T1 and T2 can be indicative of change associated with disease, pathology, or other biological process (neurodevelopment, learning and neuroplasticity, or aging and neurodegeneration). For example, the lipid-rich myelin sheath and associated proteins, cholesterol, iron-containing oligodrendrocytes and glial cells, combined with reduced free water content, are primarily responsible for the shorter T1 and T2 of white matter compared with gray matter.2 Similarly, differing concentrations of iron, principally in the form of ferritin, gives rise to the T1, T2, and T2* variations observed between deep gray matter structures.3,4 Developmental changes, including myelination, demyelination, axonal growth, and gyrification, as well as pathological processes, including edema, inflammation, tumor infiltration, iron accumulation, and necrosis, all alter the local tissue structure and biochemistry. Consequently, these processes can result in substantial relaxation time changes.2,5-9
Although seemingly obvious, it was not until 1971, some 33 years after the discovery of the nuclear magnetic resonance phenomenon,10 that disparate tissues were shown to have differing relaxation characteristics and that they were altered by pathology.
ACCELERATED METHODS FOR T1 AND T2 MEASUREMENT
While the criterion standard approaches to T1 and T2 measurement remain multiple inversion time inversion recovery (IR) and multiple echo-time spin-echo, respectively, these methods require lengthy acquisition times making them clinically prohibitive. The desire to study pathology using quantitative T1 and T2 metrics, however, has spurred the development of alternative, more rapid mapping approaches.
Of potential T1 mapping strategies, perhaps the most widely used is the method of Look and Locker.11 Proposed in 1970, this technique offers a subtle but important difference from the conventional IR approach. Rather than allowing the longitudinal magnetization to fully (or mostly) recover between successive inversion RF pulses, as in IR, the Look-Locker approach continuously samples the recovering magnetization with a series of small-angle RF pulses. Thus, the T1 recovery curve is fully characterized after a single inversion pulse. Owing to the disturbing nature of the RF pulses, the magnetization recovery follows an apparent T1, or T1*, related to T1 in a known way, and reaches a steady state that is different from the thermal equilibrium value.
An alternative to the Look-Locker method, which does away with the inversion pulse and the concept of measuring a "true" recovery curve, is the method of variable flip angles, or driven equilibrium single-pulse observation of T1 (DESPOT1).12 Here a series of spoiled gradient recalled echo (SPGR) or spoiled fast low-angle single shot (FLASH) are acquired over a range of flip angles (α) while the repetition time is held constant. Provided adequate transverse magnetization spoiling is used (or TE >> T2), these data provide a signal intensity curve characterized only by T1 and ρ. Christensen et al12 first proposed the use of this unconventional T1 "recovery curve" to characterize T1, and the method has been subsequently refined and optimized by a number of authors.13-18
Similar in concept to the variable flip angle method for T1, variable flip angle fully balanced steady-state free precession (bSSFP)19 acquisitions can also allow calculation of T2. With constant echo and repetition times, the bSSFP signal-versus-α curve is characterized by T1, p, and T2. If T1 is known a priori, T2 can be readily calculated.16
The mixed contribution of T1 and T2 to the bSSFP signal, however, also provides opportunity for their combined calculation. With the addition of an inversion pulse, to increase the T1 dependence, a sequence analogous to the Look-Locker approach is obtained.20-22 Here, the signal is driven back to an altered equilibrium via an apparent T1, T1*, related to the true T1 and T2. From this complex relationship, both T1 and T2 may be estimated.
Additional methods, which make use of rapid echo planar or spiral imaging techniques to further increase imaging speed, have also been proposed and are routinely used (eg, Preibisch and Deichmann,23 Shin et al,24 Ropele et al,25 Huang et al26).
Using these, as well as other less conventional approaches, T1 and T2 alterations have been investigated in a number of neurological disorders and pathology, offering insight into disease-related alterations in tissue microstructure.
QUANTITATIVE RELAXOMETRY IN THE CLINICAL AND NEUROSCIENCE REALMS
Although formulas relating tissue T1 and T2 to the biochemistry and structure of the tissue remain to be established, this fundamental link underpins the utility of quantitative T1 and T2 imaging to identify, investigate, diagnose, and monitor pathology. Within the brain, the utility of T1 and T2 has been previously demonstrated in multiple sclerosis (MS), epilepsy, dementia, as well as neurodevelopment.
Perhaps one of the most successful illustrations of quantitative relaxometry applied clinically is in the context of MS. Characterized by focal white and gray matter lesions in the brain and spinal cord, MS is a neurodegenerative and neuroinflammatory disorder. White matter lesion areas correspond to areas of damaged, reduced, or lost myelin. While gray matter lesions are also present, their role in the disorder is less well understood and, until the recent increased prevalence of high-field strength (ie, 7 T) scanners, has only been visualized through histological means.
The loss of the lipid-rich myelin sheath, and its subsequent replacement by free water, inflammatory cells, and other proteins, results in substantive focal alteration in T1, T2, and T2*. Lesion areas, therefore, typically present as hypointense on T1-weighted or hyperintense on T2-weighted images. On the basis of the known influence of lipid, macromolecule, and free water content on T1 and T2, relaxation times have been proposed as potential surrogate markers of disease activity in treatment monitoring and therapeutic trials. Although alterations within lesion sites have produced only weak associations with disease extent and activity, investigation of T1 and T2 in the surrounding "normal-appearing" or abnormal "dirty-appearing" white matter has revealed widespread and global alterations in T1 and T2, suggestive of unseen tissue disruption.27,28
Because MS is inherently a disorder of myelin, a more direct investigation of myelin content could provide a more tangible assessment of disease activity. In the preceding discussion, we assumed the magnetization relaxation in each image voxel was adequately described by a single T1 or T2 time. In complex biological systems, such as brain parenchyma, water is highly compartmentalized into discrete anatomical subdomains, each with unique biophysical and biochemical properties. These differing characteristics impart unique T1, T2, and T2* relaxation properties onto the water protons residing within them. Thus, each anatomical domain provides a distinct MR signal signature, with the overall measured signal representing a volume-weighted average of these individual contributions. Further, if the boundaries between these compartments are water permeable, water may readily exchange between them. The resulting complex signal decay is ill described by a single T1 or T2 value.29
Multicomponent relaxometry (MCR) analysis (illustrated in Fig. 5) attempts to model these independent signal contributors, providing a potent method for investigating subvoxel tissue microstructure.30-32 Within brain and spinal cord, MRC analysis of T2 and, more recently, T1 and T2* relaxation data has yielded 2 distinct and reproducible water environments. On the basis of concomitant imaging and histology studies,33,34 the identified compartments are broadly attributed to the less-restricted intracellular and extracellular water and water trapped within the lipid bilayers of the myelin sheath. The ability to noninvasively quantify this myelin-associated compartment immediately suggests that MCR might be useful in the study of MS (Fig. 6).
Multicomponent relaxometry, therefore, has played a significant (albeit to date, a primarily research) role in investigating myelin loss associated with MS disease progression.35-40 In addition to expected focal myelin reductions in lesion areas, MCR has revealed significant reductions in myelin content with normal-appearing white matter and has shown the potential to discriminate between acute, chronic and active, and chronic and inactive lesion subtypes. The reader is referred to a more thorough review of MCR in MS provided by MacKay et al,41 a pioneer in the field of MCR analysis.
Hippocampal sclerosis (HS), or atrophy of the hippocampus, is the most common cause of temporal lobe epilepsy. Although HS is usually associated with increased signal intensity in T2-weighted images, the ambiguity of T2-weighted signal changes hinders definitive diagnosis.42 As an adjunct to conventional weighted acquisitions, quantitative T2 imaging in normal and pathological hippocampal tissue has proven an effective method for detecting and monitoring hippocampal structure changes.43
Prolongation lobe T1 and T2 in the temporal lobe (Fig. 7), as well as throughout the brain hemisphere containing the seizure focus, has been reported in patients affected by epilepsy compared with healthy controls.44,45 These results intimate a potential role for quantitative T1 imaging in conjunction with electroencephalography in identifying and localizing seizure foci.
Dementia and Alzheimer Disease
T1 and T2 have been found to be altered in most dementia subtypes, including vascular dementia, dementia with Lewy bodies, and Alzheimer disease (AD). The iron composition of β-amyloid plaque deposits, the classic neuropathological hallmark of AD, has been proposed as a sufficient contrast mechanism to allow their visualization. Unfortunately, to date, direct plaque visualization has only been possible in animal models or in vitro specimens,46 or at ultra high field strengths (ie, 7 T). Measurement of more diffuse changes in T2 within the hippocampus and basal ganglia, caused by aggregate accumulation of plaques, however, may reduce the need for direct plaque visualization.47
In addition to plaque deposits, white matter hyperintensities are also commonly observed in AD.48 While the exact mechanisms underpinning these white matter changes remain unknown, a predominant hypothesis suggests a vascular origin.49 A recent disease model forwarded by Bartzokis et al,50 however, suggests that white matter and demyelination may play an underlying etiological role in this traditionally gray matter centric disorder. Indirect support for this myelin hypothesis has been the observation of T2 increases (presumably due to decreased myelin and increased free water content) throughout the white matter of subjects reporting memory loss and confirmed AD patients.51
Neurodevelopment and Healthy Aging
An area of increasing clinical interest is brain development in early infancy. One hypothesized substrate for a variety of psychiatric disorders, including autism, developmental delay, and attention deficit and hyperactivity disorder, is disrupted or abnormal connectivity of the complex neurological systems that underlay higher-order emotional, social, or behavioral functions.52,53 Mediating this connectivity are the myelinated white matter pathways, which develop throughout the first years of life. Quantitatively monitoring the maturation of these pathways, in association with behavior, may offer new insights into the spatial and temporal origins of these disorders.
The earliest reports of magnetic resonance imaging in neurodevelopment demonstrated progressive changes in white and gray matter contrast on conventional T1- and T2-weighted imaging over the first year of life.2,54-56 The decrease in both T1 and T2 throughout the first years is believed to reflect to the increased presence of lipids, cholesterol, and other constituents of the myelin sheath and reduced free water content (ie, increased water compartmentalization). Unfortunately, the proliferation of multichannel and surface RF coil arrays, and their associated inhomogeneous signal profiles, makes appreciation of tissue signal and contrast changes difficult and ambiguous. Further, qualitative comparisons of signal intensity are inherently limited.
Quantitative evaluation of T1 and T2 throughout neurodevelopment can provide a less-ambiguous appreciation of age-related change and maturation and can highlight the different sensitivities of T1 and T2. For example, Figure 8 shows a comparison of qualitative T1-weighted SPGR images and T1 and T2 maps from 11 healthy infants between the ages of 3 and 11 months.57
Investigations of relaxation measures across the life span has been presented by Saito et al,6 demonstrating not only the expected rapid decrease in T1 and T2 during the first 5 years of life, followed by a more shallow decrease until approximately 35 to 40 years, but also a subsequent increase in relaxation parameters into old age6 (Fig. 9). This increase, which Bartzokis et al58 have correlated with age-related reductions in processing speed, is thought to reflect a loss in brain myelination and other neurodegenerative processes.59 It should be noted, however, that Bartozkis' work is based on single-component (ie, global) T2 measures, increases of which are hypothesized to correspond to decreases in myelin content. Comparing T2 and MCR myelin water fraction estimates in healthy infants, Deoni et al60 showed poor correlations between T2 and myelin water fraction, casting doubt on Barkzokis' straightforward interpretation.
More direct assessment of myelination throughout the first months of life can also be performed using multicomponent relaxometry analysis. Figure 9 shows representative myelin fraction maps obtained from different healthy male and female infants across the first 11 months. Alternative imaging methods, including diffusion tensor and magnetization transfer imaging, have also been proposed as myelination monitoring methods.
With knowledge of the "healthy" life span T1 and T2 trajectories, quantitative comparisons with neurodevelopmental, as well as neurodegnerative, disorders may be performed and may improve our understanding of age-related brain change.
SOURCES OF ERROR IN T1 AND T2 MEASUREMENTS
The clinical utility of relaxation time imaging, however, depends on a number of factors. Foremost among these is the ability to provide data with high reproducibility and accuracy. While rapid T1 and T2 mapping techniques afford clinically feasible imaging times, a number of sources of error must be considered and accounted for to ensure the required reproducibility.
Flip Angle Inhomogeneity
Accurate and precise relaxation time measurement requires careful consideration of potential sources of error and corrective techniques to mitigate their effects. The more egregious pitfalls include flip angle error, residual or incoherent transverse magnetization, and flow and movement.
Accelerated measurement techniques generally use small flip angle RF pulses to sample the magnetization and incorporate the flip angle value itself into the T1 or T2 calculation. Thus, accurate knowledge of the applied flip angle is essential for correct T1 or T2 estimation. Deviations of the transmitted flip angle from the intended (ie, "prescribed") value arise from 2 main sources: RF pulse profile errors and RF attenuation and tissue dielectric effects.
The ideal excitation profile is a boxcar or rect function, providing the desired flip angle across the excited volume or slice and zero elsewhere. Unfortunately, the achieved profile is less than this ideal, with the transmitted flip angle varying across the volume or slice. For 3-dimensional volumetric acquisitions, this profile effect can be tolerated if the anatomy of interest lies within the center portion of the excited slab, where the flip angle is approximately uniform and of the desired value. For single and multiple 2-dimensional slice applications, this profile effect will yield variation through the image slice, with the measured signal becoming an integrated function of flip angle.61
Radiofrequency coil geometry and RF attenuation and dielectric resonance effects also lead to deviations of the intended flip angle throughout the image volume. Asymmetric RF coils have nonuniform RF power profiles resulting in the flip angle varying with distance from the coil. Finally, dielectric resonance (RF penetration) effects also result in variations in the transmitted flip angle and increase in severity as the main magnetic field strength increases.
Minimization of flip angle-related errors can be achieved through improved RF pulse design,62 use of B1 insensitive pulses,63,64 numerical RF modeling,61 or calibration of the transmitted flip angle field.65 Optimized RF pulse design, such as SLR pulses,62 provides idealized excitation profiles, minimizing the fall-off regions at the edges of the slab. In single-slice applications, Parker et al61 showed how the flip angle could be numerically modeled and accounted for in T1 measurements. Composite or fast passage adiabatic pulses, which are less sensitive to RF amplitude, offer more uniform flip angles profiles. However, these pulses require lengthy pulse durations and can have high-energy deposition.
Quantitative measurement of the transmitted flip angle has received increased attention with the move to higher magnetic field strengths and has benefited from a proliferation of rapid techniques. The most common of these techniques, the double-angle approach, acquires 2 spin-echo images with α and 2α flip angles. Through a trigonometric relationship, α is determined through the ratio of signal intensities. Rapid volumetric approaches, such as those of Jiru and Klose,66 Morrell,67 Cunningham et al,68 and Deoni,69 also provide robust calculation of the flip angle field and are fast enough to be used in combination with a quantitative T1 or T2 experiment.
Residual and Incoherent Transverse Magnetization
For accurate T1 measurement, the influence of T2 and T2* effects must be minimized. Stimulated echoes are echoes arising from previous RF pulses. Generally, when the interpulse time (TR) is greater than 5 × T2 (and T2*), little phase coherence remains among the proton spins. Thus, the transverse magnetization is dephased or naturally spoiled. However, when TR is much less than T2, as is usually the case with accelerated T1 measurement techniques, residual transverse magnetization remains at the end of the TR interval. This remaining magnetization, if left unchecked, will introduce undesired T2 weighting into the signal. This magnetization may also give rise to stimulated echoes,70 further corrupting the signal.
In addition to residual transverse magnetization at the end of the TR interval, incoherence of the transverse magnetization can further corrupt T1 and T2 measures. In some sequences, such as bSSFP, the transverse magnetization is not destroyed at the end of each TR interval. Rather, it is left to build up over multiple TR intervals. However, if the phase of the transverse magnetization is not the same after each TR period, the magnetization does not add perfectly. This results in the well-known bSSFP banding artifact19 and affects any relaxation measurement based on this signal.
Residual and incoherent transverse magnetization, therefore, will corrupt the T1 and T2 estimates made using the Look-Locker, DESPOT1, DESPOT, and IR-SSFP methods. To eliminate residual magnetization, a combination of RF71 and gradient spoiling72 is generally used and is a common and essential feature of most SPGR and spoiled FLASH pulse sequences. Eliminating incoherence within the magnetization is more difficult (requiring a near perfectly uniform magnetic field throughout the object). One common approach to dealing with bSSFP off-resonance and banding artifacts is the use of a technique called RF phase cycling73 (Fig. 10). Deoni74 has shown how this technique can be used to calculate artifact-free T2 maps through a more complete modeling of the bSSFP signal.
Bulk Movement and Flow
Subject motion has expected consequences on image quality, namely, ghosting and blurring artifacts, which can similarly bleed through the calculated relaxation time maps. Motion can be particularly troublesome in single-slice acquisitions, where the slice location may differ between inversion times, echo times, or flip angles. Beyond bulk motion, physiological motion including blood flow can introduce more subtle artifacts and T1 and T2 biases.
Accelerated measurement methods that use steady-state imaging techniques require the establishment of a magnetization steady state. In some methods, thorough spoiling of the transverse magnetization is also necessary. In moving tissues, these conditions may be violated. Depending on the extent and rate of flow, spins within blood may have exited the volume before reaching a steady state. Further, the flow of spins through the imaging and spoiling gradients may result in incomplete spoiling or, worse, refocused magnetization.
In volumetric applications, the flowing magnetization is more likely to evolve to steady state as it navigates the image volume, leaving only subtle artifact near the leading edge of the volume. In single-slice or multiple 2-dimensional slice applications, this artifact can be far more serious. Saturation bands can be used to null the flowing signal immediately outside the slice of interest. However, it is unlikely that flowing blood will achieve adequate steady state before exiting the slice, making 2-dimensional approaches ill suited to quantify blood T1 or T2.
Inadequate Steady State
Most accelerated T1 and T2 measurement techniques use steady-state imaging techniques. In methods based on rapid SPGR or bSSFP acquisitions, for example, the magnetization is first driven to a dynamic equilibrium and then sampled. Establishment of this steady state generally requires a duration of T1, although catalyst approaches may reduce this time,75 during which nonsampling "dummy" pulses are applied. Failure to ensure adequate steady state before acquisition can result in signal oscillations throughout the k-space (presenting as ghosting in the reconstructed image) and incorrect T1 or T2 estimation (as the steady-state signal models become inappropriate).
With suitable appreciation for these principal sources of error, artifact-free T1 and T2 maps may be obtained with high accuracy and reproducibility. Such maps enable a wide range of analysis and direct comparisons, which may identify regions of change not evident on conventional images.
ANALYSIS AND INTERPRETATION OF RELAXATION DATA
Depending on the spatial extent and resolution of the acquired maps, comparisons can be at the whole-brain, hemispheric, regional, white matter tract, or voxelwise levels. In addition to group comparisons, a powerful attribute of relaxation data is the ability to perform single-subject comparisons against population norms without requiring correction for scanner hardware, acquisition strategy, and others.76
Most clinical and research structural neuroimaging studies involve normal pathology comparisons to determine (1) if there is a difference in brain structure associated with the condition, (2) where in the brain those differences are manifest, and (3) how identified differences vary with degree of pathology. To address these basic questions, a number of approaches, each with differing levels of sensitivity and detail, have been devised.
Histogram-based approaches offer a straightforward means of addressing the most basic question: is there a difference between normal and disease?77 Advantageous as they require no spatial normalization (alignment of images from each participant) and require no a priori hypothesis as to where changes might be expected, histograms provide an intuitive and direct means for visualizing group differences.
Calculation of T1 or T2 histograms, simply the frequency of binned values, is straightforward. Correction for brain volume is accomplished by normalizing the bin frequencies by the total number of voxels included in the histogram (or area under the histogram curve). Averaged patient and control histograms can be visually and statistically compared, with standard metrics of comparison including mean, median, mode, skewness, kurtosis, peak height, and peak location. An example of histogram-based comparison of white matter T1 and T2 in healthy adolescents and adolescents with autism is shown in Figure 11.
The primary disadvantages of histogram-based comparisons are that they provide no spatial information as regard to where identified differences exist and are only sensitive to large-scale (global) tissue changes because focal changes are likely to be obscured when included with whole-brain data.
The primary disadvantage of histogram-based approaches is the lack of spatial information. An approach to address this deficit of information is to compare values within predefined regions of interest, or more generally, to treat each voxel as an independent region of interest and perform voxelwise comparisons.78
After linear or nonlinear spatial normalization of patient and control image data,78,79 voxelwise t tests (or a nonparameter equivalent) with appropriate correction for multiple comparisons78,80 can identify regions of group difference. The ability to examine the whole-brain, without requiring consideration of a priori hypotheses, offers tremendous potential for speculative or exploratory studies where affected regions may not be known before hand.
An example of voxel-based comparison of T1 and T2 in schizotypy is shown in Figure 12, demonstrating hemispheric differences in the relaxation times in normal versus high-schizotypy groups.
A common theme in neuroimaging research is the use of a connectionist approach to understand neurological or psychiatric disorders.81 This approach entails consideration of the white matter tracts that connect the disparate brain regions comprising integrated neuronal networks and brain systems. While voxel-based approaches can elucidate regions of differences, these regions may contain multiple independent white matter pathways connecting different gray matter regions. Thus, additional information may be gleaned by considering the T1 and T2 characteristics along specific tracts of interest.82,83
Two common approaches for isolating specific white matter pathways are the use of digitized atlases84,85 or the combined acquisition of relaxation and diffusion tensor imaging data.86 Diffusion imaging provides estimates of local fiber orientation.86 By stitching these independent orientation measures together (tractography), 3-dimensional representations of the white matter paths may be reconstructed.87
Using either atlas or tractography data to supply regions of interest, T1 and T2 values within these regions can be obtained and statistical comparisons can be made. As an example, Figure 13 shows comparison of T1 and T2 histogram data for the left and right superior longitudinal fasiculi in patients with autism and healthy age and sex-matched controls.
Comparison With Population Norms
In a variety of disorders, groupwise comparisons are difficult or ill-posed. For example, MS is characterized by acute focal white and gray matter lesions occurring throughout the brain and spinal cord.88 In many instances, however, from a disease monitoring or prognosis perspective, it is not the lesions themselves that are of interest but the surrounding normal-appearing white matter.27 Voxelwise comparisons across groups is not appropriate given the near-random location of lesions. While histogram-based approaches are useful, they lack the spatial information necessary to pinpoint affected brain regions. In these cases, it is preferable to perform subject-specific analysis, comparing each subject with a matched population average.
Similar to groupwise comparisons, the population mean and variance can be determined from spatially normalizing healthy participant data (matched for age, sex, handedness, etc, as required). Identification of voxels or regions that differ substantively from the population norm can be determined through straightforward voxelwise z tests. Illustrated in Figure 14 comparison of T1 data from an MS against a matched population average demonstrates how this form of analysis can reveal white matter regions that appear normal on conventional clinical T1- and T2-weighted scans that are, in fact, substantially affected.89
In addition to MS, conditions characterized by sparse or random pathology, for example, stroke and cancer, may benefit from this form of analysis.
Quantitative imaging offers a number of advantages over more conventional qualitative or weighted imaging approaches, including simplicity of analysis, quantitative and population-based comparisons, and more direct interpretation of detected changes. Recent acquisition techniques coupled with appreciation of, and correction for, relevant sources of error have made the acquisition of whole-brain high-spatial resolution quantitative T1 and T2 maps a clinically feasible alternative to conventional T1- or T2-weighted imaging. Analysis of these maps is in its infancy; however, we can draw inspiration from other quantitative imaging techniques, such as proton emission tomography, to glimpse the rich information contained within these data. Further, when combined with alternative MR-based imaging techniques, including diffusion tensor MRI and MR spectroscopy, a more complete picture of brain structure, architecture, and metabolism may be formed. Imaging protocols comprising each of these elements will be ideally positioned to assess tissue alternation in pathology.
A special thanks is extended to those who provided clinical examples and imaging data used herein: Prof Derek Jones, Dr Janneke Zinkstok, Dr Marco Catani, Dr Emma Burkus, Dr Mark Richardson, Katrina McMullin, Catherine Traynor, and Sarah Kwan.
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