Ana-Maria Oros1, Anna Weglage1, and N. Jon Shah1,2,3,4
1Institute of Neuroscience and Medicine (INM-4), Research Centre Juelich, Juelich, Germany, 2JARA-BRAIN-Translational Medicine, Research Centre Juelich, Aachen, Germany, 3Institute of Neuroscience and Medicine (INM-11, JARA), Research Centre Juelich, Juelich, Germany, 4Department of Neurology, RWTH Aachen University, Aachen, Germany
Synopsis
Tissue T1 relaxation
is considered to be monoexponential. This is, however, an assumption, since it
is seldom measured with sufficiently dense sampling of the relaxation curve. We
hypothesized that a spatially-resolved investigation of tissue T1 relaxation
curve with ultra-high temporal resolution might reveal previously unidentified
characteristics of this fundamental NMR parameter. We imaged 10 healthy
volunteers using a Look-Locker sequence with 460 time points 17ms apart on the
inversion recovery curve. Denoising using PCA and NNLS decomposition of the
recovery curves revealed, among others, a moderately short T1 component
(4-500ms) in both WM and GM, tentatively assigned to myelin water.
Introduction
NMR relaxation times are known to reflect microscopic tissue properties, albeit
in a convoluted way.
T2 relaxometry in human white
matter reproducibly detects a fast-relaxing component (15ms) attributed to
myelin water1 and histologically validated as a myelin marker2.
Conventionally, T1 relaxation is considered monoexponential. However, a small
number of studies report identification of T1 multicomponent structure3,4
in healthy brain tissue.
A methodological difference exists between the
investigation of T2 and T1 relaxation curves. Whereas for spin-echo
acquisitions of T2 it is feasible, even clinically, to acquire 32 and more
echoes per phase-encoding line, it is more time consuming to do so for T1
relaxometry. The reported number of T1 points acquired for investigations of
multicomponent relaxation can reach 1285 , but not in conjunction
with imaging. Furthermore, the points are often logarithmically-spaced,
covering a broad T1 interval (ms-s), leaving relevant T1 regions insufficiently
sampled.
We hypothesized that a spatially-resolved investigation of
the T1 relaxation curve with ultra-high temporal resolution might reveal previously
unidentified characteristics of this fundamental NMR parameter. Materials and Methods
Ten healthy volunteers (5 female, 32.4±0.3yrs) were measured
on a 3T MR scanner following prior, written informed consent. Quantitative MRI
was performed using a Look-Locker sequence, TAPIR
6,7, including
mapping of the inversion efficiency. The inversion-recovery curved was sampled
in steps of 17ms with 460 time-points within a single-slice TA of ~4 min (Table
I). Multi-contrast magnitude and phase data were saved and processed off-line. A
‘noise scan’ was acquired with the same imaging parameters by setting the RF voltage
to zero.
Analysis of signal characteristics and denoising of magnitude
and phase data was performed using principal component (PCA) decomposition of
the complex data
8, found to be very stable over the 10 volunteers.
Only 3-5 components out of 460 contained information relevant to signal decay.
For each volunteer, the number of signal-containing components was assessed
visually and also inferred from random matrix theory analysis of the
distribution of eigenvalues
9. Furthermore, a comparison of
eigenvalue spectra from signal and noise regions and from acquisitions of noise
only was performed and characteristics of noise spectra identified. Noise-only
components were discarded. Monoexponentially fitting the TAPIR signal
7
delivered maps of the longitudinal relaxation time T1, signal intensity at TI=0
and flip angle. The effect of data denoising on fit parameters was investigated.
NNLS analysis using 200 logarithmically spaced T1 values between 50ms and 6s
and Tikhonov regularisation was performed on the original, noisy data, and after
denoising. Masks for different tissue types were defined using simultaneously
acquired MPRAGE and SPM-based segmentation. The components identified in the T1
spectra are reported per tissue class and their distribution visualised as maps
of component fractions.
Results
The salient features of the PCA decomposition are illustrated
in Figs. 1-2. Fig.1 describes the behaviour of eigenvalues obtained from analysing the signal
from the brain region and the corresponding characteristics of the noise
spectrum; the latter is obtained either from regions void of signal, or from
the noise acquisition. The first 6 principal
components are shown in Fig.2a and their temporal characteristics in Fig.2b,
separately for magnitude and phase. The effects of denoising are characterised
in Fig.3 for the signal obtained from a single GM voxel and the images at time
point 19 (TI=343ms) (Fig 3a), and the T1 and M0 maps
obtained by monoexponential fitting of the data (Fig.3b).
Fig.4 shows the results of NNLS analysis of the denoised
data, with Tikhonov regularisation (chi2<1.025*chi2_orig), in spectra and
image form.Discussion and Conclusion
In practically all WM and GM voxels a short T1 component was
identified, with T1~400ms (WM) and ~500ms(GM), also when using original low-SNR
data. In the absence of regularisation, an additional, even shorter component
might be present; however, only the longer short component survives
regularisation. The presence of a transient, MT-induced, short relaxation
component in Look-Locker acquisitions is well known10. However,
biexponential decay due to MT only affects the first 60-100ms on the inversion
curve10, giving a T1 component with an order-of-magnitude shorter
time constant than the ~500ms found here.
In conclusion, a distinct, moderately short T1 component is
robustly identified in vivo, arguably
for the first time. The maps reflecting its spatial distribution show
unprecedented SNR and detail for multicomponent relaxometry, even when analysis
is performed on the original (SNR=10) data. While in WM a single additional
peak was identified in most cases, GM shows a more complicated T1 spectrum. The
precise nature of these components requires further investigation11.Acknowledgements
No acknowledgement found.References
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