Yuki Takai1 and Tokunori Kimura2
1MRI development department, Toshiba Medical Systems, Otawara, Japan, 2Clinical Application Research and Development Dept., Toshiba Medical Systems, Otawara, Japan
Synopsis
We proposed a new computed DWI technique allowing
to provide quantitative maps of ADC, T2, T1 and DWI images with arbitrary contrast of PDW, T2W, and T1W. We clarified that our techniques enabled to provide
FLAIR-DWI images with higher SNR than actually measured FLAIR-DWI, and furthermore enhanced
by using SE-based ADC map and SE- or IR-based T1 map with optimal TI for brain
tissue. Although further optimization is required, it is expected to be especially
useful for clinical brain diagnosis.Purpose
A computed diffusion imaging
(cDWI), which allows to provide high b-value (b) equivalent DWI images using relatively low b DWI images, was proposed and applied to improve contrast between
tumors and the background tissue especially in body diffusion imaging [1-3]. More
recently a short-TE cDWI technique was proposed to reduce T2 shine-through
effects or to further enhance tissues with short T2 and lower ADC, where DWI
images with arbitrary combination of TE (including zero) and b can be generated [4]. On the other
hand, a synthetic MRI technique has proposed and gathering attention where spin
echo (SE)-or inversion recovery (IR)-based images with arbitral imaging
parameters with shorter acquisition time than actual imaging [5].
The
purpose of this study was to propose a new computed DWI technique to generate arbitral
combination of imaging parameters of TR (or TI), TE and b in combination of 4 kinds of acquired
images, and to assess for volunteer brain imaging.
Here Fluid-Attenuated IR (FLAIR)-DWI imaging was particularly focused on as an
application of this technique.
Methods
Theory
Spin echo (SE)-based measured DWI (mDWI) signal intensity at TR, TE
and b for tissue of T1, T2 and ADC is modeled by using arbitrary coefficient k
as:
S(TR,TE,b)=k*(1-exp[-TR/T1])*exp[-TE/T2]*exp[-b*ADC] ---- (1),
or IR-based mDWI signal
intensity at TI, TE, and b when TR is infinity is modeled as:
S(TI,TE,b)=k*(1-2 exp[-TI/T1])*exp[-TE/T2]*exp[-b*ADC] ---- (2).
Algorithm
for 4-point cDWI method based on these models was shown in Fig.
1. Here following methods for computed FLAIR-DWI were compared including measured FLAIR-DWI.
a) FLAIR-mDWI: 1-point directly acquired IR signal
at T2W-FLAIR DWI condition was assumed.
b) ADCbyFLAIR-cDWI: ADC
was calculated with 2 points FLAIR signals setting at TI1=T1 of CSF.
c) ADCbySE&T1bySE FLAIR-cDWI: ADC
was calculated with 2 points T2W-SE signals. T1 was calculated using 2 points signals T1W-SE setting at TR1=T1 of brain tissue and PDW-SE.
d) ADCbySE&T1byIR FLAIR-cDWI: ADC
was calculated with the same way as method-c. T1 was calculated using 2 points signals of T1W-IR setting at TI1=T1 and PDW-SE.
Monte-Carlo simulation
Assuming Rician
distribution of noise on the magnitude DWI signals, the mean and the SD of quantitative
parameters and cDWI signals were measured after 5000 times each trial for 4
methods using the parameters shown in Table
1. The k in Eq.(1-2) was 1 and the added
Gaussian noise SD for mDWI was 0.02.
Here TE was commonly set at TE2 for T2W condition. The mean, SD and SNR assuming brain tissue were compared.
Volunteer study
Imaging was
performed on 3T MR imager of Toshiba Vantage Titan 3T with a single-shot
SE-EPI with the same parameters in Table 1
except for TI1=2000ms and TE2=80ms. Motion probing
gradient (MPG) was applied to a direction perpendicular to the running
direction of targeted fibers (corpus callosum (CC)). The cDWI images of PDW and
T2W each with bc=0, 1000 and 2000 s/mm2 were calculated, where
TIc was experimentally decided at 2000ms.
Results
For simulations, the background
noise SDs
were increased in the order of method d
<c <b <a (
Fig. 2) reflecting
noise propagation effects of quantitative parameters.
Table 2 shows the summary of the simulation. As the SDs in ADC
and in R1 were smaller, SDs in cDWI
signals became smaller; i.e., the SDs in ADC obtained by SE (b, c, and d) were smaller than by the T2W-FLAIR (a), and the SDs in R1 obtained
by IR(d) were less than those by SE(c). In addition, noise bias effects on DWI signals due to Rican noise were
significant in high b
for FLAIR-mDWI.
Regarding
MRI experiments (
Fig. 3), FLAIR
image of PDW (TEc=28ms) and T2W (TEc=80ms) were shown in
addition to measured images and quantitative maps. Brain tissue provided
sufficient SNR even using T1bySE method despite of poor T1W image. The SNR for the R1 map with T1byIR was
better than with T1bySE, and therefore those effects reflected on the corresponding
cDWI images. It was good news that the CSF signals in FLAIR-cDWIs became
negative without using phase correction even using T1bySE.
Conclusion
We proposed a new computed DWI technique allowing
to provide quantitative maps of ADC, T2, T1 and DWI images with arbitrary contrast.
We clarified that our techniques enabled to provide FLAIR-DWI with higher SNR
than actually measured FLAIR-DWI suffering from SNR compared to SE-DWI, and furthermore enhanced by using SE-based
ADC map and SE- or IR-based T1 map with optimal TI for brain tissue. Although further
optimization or systematic design of software including user interface are
required, it is expected to be especially useful for clinical brain diagnosis.
Acknowledgements
No acknowledgement found.References
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