MultiBand SENSE starts with the simultaneous excitation of two or more slices, while the acquisition readout is unchanged. So, the base resulting image is actually an accumulated image of all excited slices. However, similar to normal SENSE algorithms, the signal can be unfolded to reveal the separate images.
This unfolding can be complicated when coil sensitivity profiles are similar for the separate slices. Therefore, the MultiBand SENSE technique employs a phase shift during excitation to simplify the unfolding process, and virtually eliminate artifacts generated by residual aliasing and noise enhancement [5].
The result is that MultiBand SENSE can acquire multiple slices in a time identical to that of a single slice acquisition, which thus provides a significant acceleration. The acceleration is chosen via the MultiBand SENSE factor that indicates the number of simultaneously acquired slices, which is always an integer number.
Dr. Caan indicates that also diffusion imaging benefits from MultiBand SENSE. “We can speed up imaging and acquire more data in the same amount of time. In other words, it provides more statistical power within the same measurement time, which helps to perform better model fits, and get more precise parameter estimates. Or we can perform studies with smaller groups, something that was not possible previously.”
According to Dr. Caan, the diffusion protocol with MultiBand SENSE uses four b-values up to b 2200, 164 gradient directions, 58 slices in 16 minutes. “In this protocol, we use a MultiBand SENSE factor of 3. We found this to provide our preferred homogeneous image quality, for instance when acquiring data in transverse orientation, and then looking at the coronal plane.”
Default mode network as discovered by resting state fMRI in one participant of the ALFA cohort [7]. rs-fMRI allows us to find networks of brain regions with highly correlated activity and sustaining distinct brain functions. The default mode network (in warm color scale) is active when the brain is focused on introspective thinking and has been shown to be altered in Alzheimer’s. Interestingly, brain areas of this network are known to show abnormal levels of one of the pathological hallmarks of Alzheimer’s (b-amyloid deposition) in preclinical stages. We want to better understand the alterations of these brain networks in preclinical stages of Alzheimer's and explore their potential use as biomarkers.
Imaging was performed using Ingenia 3T CX with a 32ch dS Head coil, TR 1.6 sec, TE 35 ms, voxel size 3.1 x 3.1 x 3.1 mm, 46 slices and Multiband SENSE factor 2. Image provided by Dr. Gispert
1. Nir TM, Jahanshad N, Toga AW, Berstein MA, Jack CR Jr., Weiner MW, Thompson PM. The Alzheimer’s Disease Neuroimaging Initiative (ADNI). Connectivity network measures predict volumetric atrophy in mild cognitive impairment. Neurobiol Aging. 2015;36:S113-S120.
2. Shaffer JJ, Ghayoor A, Long JD, Kim RE, Lourens S, O’Donnell LJ, Westin CF, Rathi Y, Magnotta V, Paulsen JS, Johnson HJ. Longitudinal diffusion changes in prodromal and early HD: evidence of white-matter tract deterioration. Hum Brain Mapp. 2017;38:1460-77.
3. Wang T, Shi F, Jin Y, Yap PT, Wee CY, Zhang J, Yang C, Li X, Xiao S, Shen D. Multilevel deficiency of white matter connectivity networks in Alzheimer’s disease: a diffusion MRI study with DTI and HARDI models. Neural Plast. 2016;2016:2947136.
4. Wang Y, Wang J, Jia Y, Zhong S, Niu M, Sun Y, Qi Z, Zhao L, Huang L, Huang R. Shared and specific intrinsic functional connectivity patterns in unmedicated bipolar disorder and major depressive disorder. Sci Rep. 2017;7:3570.
5. Setsompop K, Cohen-Adad J, Gagoski BA, Raij T, Yendiki A, Keil B, Wedeen VJ, Wald LL. Neuroimage. 2012 Oct 15;63(1):569-80. doi: 10.1016/j. neuroimage.2012.06.033
6. Beckmann CF. Modelling with independent components. Neuroimage [Internet] 2012;62:891–901. doi 10.1016/j.neuroimage.2012.02.020.
7. Molinuevo et al. The ALFA project: A research platform to identify early pathophysiological features of Alzheimer's disease. Alz Dem. June 2016 Volume 2, Issue 2, Pages 82–92. doi 10.1016/j.trci.2016.02.003
8. ABCD study, https://addictionresearch.nih.gov/abcd-study Philips is not sponsoring this study.
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