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NEUROLOGIC/HEAD AND NECK IMAGING
Multispectral Quantitative MR
Imaging of the Human Brain:
Lifetime Age-related Effects1
Memi Watanabe, MD • Joseph H. Liao, MD • Hernán Jara, PhD • Osamu
Sakai, MD, PhD
Quantitative magnetic resonance (MR) imaging allows visualization of age-
related changes in the normal human brain from functional, biochemical,
and morphologic perspectives. Findings at quantitative MR imaging sup-
port age-related microstructural changes in the brain: (a) volume expan-
sion, increased myelination, and axonal growth, which establish neural con-
nectivity in neurodevelopment, followed by (b) volume loss, myelin break-
down, and axonal degradation, leading to the disruption of neural integrity
later in life. A rapid growth change followed by a continuous slower change
in quantitative MR parameters can be modeled with a logarithmic or expo-
nential decay function. The age dependencies during adulthood often fit a
quadratic model for transitional changes with accelerated aging effects or
a linear model for steady changes.Understanding these general trends over
the human life span can improve assessment for a specific disease by help-
ing determine appropriate study settings. Once a consensus on acquisition
techniques and image processing algorithms has been reached, quantitative
MR imaging can play an important role in the assessment of disease states
affecting the brain.
Remarkable advances in magnetic resonance (MR) imaging have led to the de-
velopment of a variety of quantitative imaging techniques, including diffusion-
weighted imaging, diffusion-tensor imaging, relaxometry, magnetization transfer
imaging, perfusion imaging, MR spectroscopy, and volumetry (Fig 1). Diffusion-
weighted imaging, the most widely used type of quantitative MR imaging, has
a short acquisition time and has become an integral part of the standard brain
MR imaging protocol (1). It is used for the assessment of altered water diffu-
sion caused by pathologic conditions such as ischemia, trauma, tumors, infection,
inflammation, and demyelination (2). Normal or pathologic changes in brain
volume are often encountered in daily practice. Naturally, quantification in these
assessments has generated considerable interest within the radiology community.
At conventional MR imaging, normal maturation and aging of the human brain
Abbreviations: ADC = apparent diffusion coefficient, CBF = cerebral blood flow, MTR = magnetization transfer ratio, NAA = N-acetyl aspartate,
ROI = region of interest
RadioGraphics 2013; 33:1305–1319 • Published online 10.1148/rg.335125212 • Content Codes:
1From the Department of Radiology, Boston Medical Center, Boston University School of Medicine, 820 Harrison Ave, FGH Building 3rd Floor,
Boston, MA 02118. Presented as an education exhibit at the 2011 RSNA Annual Meeting. Received December 5, 2012; revision requested January
18, 2013, and received March 7; accepted March 7. O.S. has disclosed a financial relationship (see p 1317); the other authors, editor, and reviewers
have no relevant relationships to disclose. Address correspondence to M.W. (e-mail: firstname.lastname@example.org).
©RSNA, 2013 • radiographics.rsna.org
1306 September-October 2013 radiographics.rsna.org
are chiefly characterized by volume expansion
and increased myelination during childhood,
with atrophy and white matter changes during
senescence. Additional microstructural infor-
mation obtained at quantitative MR imaging
would contribute greatly to the understanding
of the mechanisms of normal and abnormal ag-
ing. Moreover, knowledge of age-related and
regional differences as seen at quantitative MR
imaging is necessary if this modality is to be-
come part of clinical practice.
In the past, many quantitative MR imaging
studies have made use of manually or automati-
cally defined regions of interest (ROIs). The
ROI study offers advantages in the regional as-
sessment of targeted structures, although its effi-
cacy tends to be affected by the hypothesis used
(eg, which ROI is to be analyzed) and by inter-
and intraindividual variations (3). For a more
global assessment, quantitative information
can be obtained with histogram analysis, which
shows the distribution of data (eg, apparent
diffusion coefficient [ADC] and T2 relaxation
times) within the selected volume. Voxel-based
analysis is increasingly being used in quantita-
tive MR imaging to investigate regional differ-
ences of the entire brain. In diffusion-tensor im-
aging, a relatively novel quantitative fiber-track-
ing technique allows sophisticated measurement
of a specific fiber bundle.
In quantitative imaging, mathematic model-
ing is often used to describe a specific age trend
for each parameter. The best-fitting models are
determined on the basis of known physiologic
features, a given hypothesis, or selection of the
model that best fits the data. The coefficient of
determination (R2) is typically used to measure
closeness of fit. Knowledge of general age trends
at quantitative imaging will facilitate mathematic
modeling in future studies. For developmental
periods with a rapid growth rate, the possible
models may include linear regression, logarith-
mic, and mono- and biexponential decay func-
tions; for transitional periods, they may include
quadratic or cubic polynomial functions. The
definitions of various mathematic models are
given in the Table, and Figure 2 illustrates sample
curves generated with these models.
Figure 1. Chart illustrates the advantages of quantitative MR imaging. The effects of aging on the
brain can be seen at conventional MR imaging, but quantitative MR imaging could play an important
role in the quantification of normal age-related changes and the assessment of microstructural changes.
ASL = arterial spin labeling, DTI = diffusion-tensor imaging, DWI = diffusion-weighted imaging.
RG • Volume 33 Number 5 Watanabe et al 1307
Definitions of Mathematic Models
y = a0 · logk(t0x)
y = a0 · exp(−t0x)
y = a0 · exp(−t0x) + a1 · exp(−t1x)
y = a0 + a1x + a2x2
y = a0 + a1x + a2x2 + a3x3
Note.—a0, a1, a2, a3, t0, t1, and k are the coefficients that influence the regres-
Figure 2. Graphs illustrate curves generated with logarithmic (a), exponential decay (b), quadratic
polynomial (c), and cubic polynomial (d) models.
RG • Volume 33 Number 5 Watanabe et al 1319
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Teaching Points September-October Issue 2013
Multispectral Quantitative MR Imaging of the Human Brain: Lifetime Age-
Memi Watanabe, MD • Joseph H. Liao, MD • Hernán Jara, PhD • Osamu Sakai, MD, PhD
RadioGraphics 2013; 33:1305–1319 • Published online 10.1148/rg.335125212 • Content Codes:
It should be noted that methodologic consensus has not been fully established in quantitative imaging,
mainly due to the lack of cross-institution or cross-vendor reproducibility.
These age-associated changes in diffusion parameters occur rapidly during early childhood and more
slowly through early adulthood, a pattern that is often described as exponential
With the growing clinical importance of diffusion-tensor imaging metrics, including significant correla-
tions between diffusion-tensor imaging signs of disrupted tract integrity and impaired cognitive-motor
performance, diffusion studies such as diffusion-tensor imaging will undoubtedly become more impor-
tant in understanding the functional and structural pathophysiologic features of the brain.
Brain aging can be characterized by four distinct periods: (a) maturation (0–2 years of age), a time of
rapid change; (b) development (2–20 years), during which time a steady slowing in the rate of develop-
ment is seen; (c) adulthood (20–60 years), characterized by little or no change; and (d) senescence (>60
years), a period of gradually increasing dysfunction.
The age dependencies during adulthood often fit a quadratic model for transitional changes with ac-
celerated aging effects, or a linear model for steady changes. These changes in quantitative MR imaging
parameters may be attributed to alterations and dissociations in neural networks, as well as to neuronal
dysfunction occurring during the aging process.