Two Figures and related images of bacterial nanowires and metal concentrations in a bacterial cell that accompany Lattice paper by Lewis Larsen concerning possibility of bacterial transmutations dated December 7, 2010.
Sensors are becoming ubiquitous in our lives and possible applications are countless. Micro and nanotechnologies are the natural choice for enabling complex sensor nodes, as they are small (thus unobtrusive), cheap and low power. Carbon nanotubes (CNTs) are a perfect example of how nanosystems offer features unachievable with microsystems: their outstanding structural, mechanical and electronic properties have immediately resulted in numerous device demonstrators from transistors, to physical and chemical sensors, and actuators. A key idea of the project is to combine elements from the fundamental knowledge base on the physics of carbon nanotubes, gathered in the past several years, and the fundamental engineering sciences in the area of micro/nano-electromechanical systems, to develop novel devices and processes based on CNTs.
Specificaly, it seeks to demonstrate concepts and devices for ultra-low power, highly miniaturized functional blocks for sensing and electronics. Due to their small mass and high stiffness, doubly clamped CNTs can exhibit huge resonant frequencies. These are carbon nanotube resonators which, as recently demonstrated or predicted theoretically, can reach the multi-GHz range, can be tuned via straining over a wide range of frequency, offer an unprecedented sensitivity to strain or mass loading, exhibit high quality factors, and all these with a very low power consumption.
Two Figures and related images of bacterial nanowires and metal concentrations in a bacterial cell that accompany Lattice paper by Lewis Larsen concerning possibility of bacterial transmutations dated December 7, 2010.
Sensors are becoming ubiquitous in our lives and possible applications are countless. Micro and nanotechnologies are the natural choice for enabling complex sensor nodes, as they are small (thus unobtrusive), cheap and low power. Carbon nanotubes (CNTs) are a perfect example of how nanosystems offer features unachievable with microsystems: their outstanding structural, mechanical and electronic properties have immediately resulted in numerous device demonstrators from transistors, to physical and chemical sensors, and actuators. A key idea of the project is to combine elements from the fundamental knowledge base on the physics of carbon nanotubes, gathered in the past several years, and the fundamental engineering sciences in the area of micro/nano-electromechanical systems, to develop novel devices and processes based on CNTs.
Specificaly, it seeks to demonstrate concepts and devices for ultra-low power, highly miniaturized functional blocks for sensing and electronics. Due to their small mass and high stiffness, doubly clamped CNTs can exhibit huge resonant frequencies. These are carbon nanotube resonators which, as recently demonstrated or predicted theoretically, can reach the multi-GHz range, can be tuned via straining over a wide range of frequency, offer an unprecedented sensitivity to strain or mass loading, exhibit high quality factors, and all these with a very low power consumption.
Time-Frequency Representation of Microseismic Signals using the SSTUT Technology
Resonance frequencies could provide useful information on the deformation occurring during fracturing experiments or CO2 management, complementary to the microseismic events distribution. An accurate time-frequency representation is of crucial importance to interpret the cause of resonance frequencies during microseismic experiments. The popular methods of Short-Time Fourier Transform (STFT) and wavelet analysis have limitations in representing close frequencies and dealing with fast varying instantaneous frequencies and this is often the nature of microseismic signals. The synchrosqueezing transform (SST) is a promising tool to track these resonant frequencies and provide a detailed time-frequency representation. Here we apply the synchrosqueezing transform to microseismic signals and also show its potential to general seismic signal processing applications.
Time-Frequency Representation of Microseismic Signals using the SSTUT Technology
Resonance frequencies could provide useful information on the deformation occurring during fracturing experiments or CO2 management, complementary to the microseismic events distribution. An accurate time-frequency representation is of crucial importance to interpret the cause of resonance frequencies during microseismic experiments. The popular methods of Short-Time Fourier Transform (STFT) and wavelet analysis have limitations in representing close frequencies and dealing with fast varying instantaneous frequencies and this is often the nature of microseismic signals. The synchrosqueezing transform (SST) is a promising tool to track these resonant frequencies and provide a detailed time-frequency representation. Here we apply the synchrosqueezing transform to microseismic signals and also show its potential to general seismic signal processing applications.
1. Short-Echo, Single Voxel 1H NMR
Spectroscopy of the Human Brain at
7T with Multi-Channel Coils
Uzay E Emir, Melissa Terpstra, Ivan Tkáč, Gülin Öz
Center for Magnetic Resonance Research
University of Minnesota
Minneapolis, Minnesota, USA
2. High-field In Vivo Single Voxel
1H NMR Spectroscopy at 7T
Increase in signal-to-
noise-ratio (SNR) NT = 128
Increase in chemical S/N = 130
x5
shift dispersion
6 5 4 3 2 1 ppm
Glc NT = 128
S/N = 264
x5
6 5 4 3 2 1 ppm
Tkáč, MRM,2009
3. High-field In Vivo Single Voxel
1H NMR Spectroscopy at 7T
Increase in signal-to- The precision at
noise-ratio (SNR) 4T for NT 128
Increase in chemical corresponds to
shift dispersion the precision at 7T
Reliable quantification for NT 4.
of up to 17 metabolites
1.0 1.5
CRLB (µmol/g)
CRLB (µmol/g)
0.8 4T Gln 1.2
Glu
4T
0.6 0.9
0.4
7T 0.6
7T
0.2 0.3
0.0 0.0
2 4 8 16 32 64 128 2 4 8 16 32 64 128
NT NT
Tkáč, MRM,2009
4. High-field In Vivo Single Voxel
1H NMR Spectroscopy at 7T
NAA
Creatine
Choline
Measurement Glutathione
Vitamin C
Lactate
Glucose
GABA
Fit
Glutamate
Glutamine
Residual myo-inositol
Taurine
4.0 3.5 3.0 2.5 2.0 1.5 1.0 ppm scyllo-inositol
•
•
•
5. High-field In Vivo 1H NMR
Spectroscopy in Several Voxels
Huntington’s disease
Parkinson’s disease
Spinocerebellar ataxia
Alzheimer’s disease
Demyelinating white matter
disease
6. Single Voxel, High-field In Vivo 1H
NMR Spectroscopy
Huntington’s disease
Parkinson’s disease
Spinocerebellar ataxia
Alzheimer’s disease
Demyelinating white matter
disease
7. High-field In Vivo 1H NMR
Spectroscopy at 7T: Challenges
Non-uniform B1 field
– long RF pulses (Chemical
Shift Displacement Error)
– B1 shimming
Short T2 relaxation times
of metabolites in the
human brain (Deelchand, 500
NAA
ISMRM, 2009) 400 Cr
T2 (ms)
300
200
100
0
0 2 4 6 8 10
Field Strength (T)
8. High-field In Vivo 1H NMR
Spectroscopy at 7T: Challenges
Non-uniform B1 field 150 ms
– long RF pulses (Chemical 80 ms
Shift Displacement Error)
50 ms
– B1 shimming
Short T2 relaxation times 30 ms
of metabolites in the
20 ms
human brain (Deelchand,
ISMRM, 2009)
– Short TE 10 ms
8 ms
5 4 3 2 1 ppm
9. Aim
To demonstrate the feasibility of short
echo single voxel 1H NMR spectroscopy in
brain regions other than occipital cortex at
7T
Phased array coil
B1 shimming
10. Methods
16-channel transmit/receive transmission line
array (Adriany,MRM,2008)
B1+ shimming (Van de Moortele,MRM,2005)
B0 shimming (FASTMAP) (Gruetter,MRM,2001)
1H NMR spectroscopy, TE=8 ms (Siemens
console)
– VAPOR+OVS+STEAM (Tkáč, MRM,2001)
– 7.5 % chemical shift displacement error
LCModel
– Water spectrum
12. Local B1+ Shimming
Relative P o s ts h im
Transmit
Efficiency P re s h im
Occipital Cortex 2.1
Posterior
1.2
Cingulate Preshim Postshim
Frontal White
1.7
Matter
Putamen 1.3
Substantia Nigra 1.1
Pons 1.2
Cerebellar
2.6
Vermis
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
13. Reconstruction of 1H NMR
Spectrum From Multi Channel Coil
Multi Channel Coil
Water Suppressed Water
Spectrum Spectrum
Phase and Eddy Current Correction Weighting Factors
Weighted Summation
Natt, MRM, 2005
14. Phase and Eddy Current
Correction
Water spectrum Water suppressed
spectrum
Phase and Eddy
current corrected spectrum
Occipital cortex
16. B0 Shimming
Volume Linewidth
of Interest [Hz]
Posterior Cingulate 11
Occipital Cortex 13
Cerebellum 13
Pons 13
Frontal White Matter 14
Putamen 16
Substantia Nigra 18
17. 1H Spectrum of Occipital Cortex at 7T
STEAM
TE = 8 ms TR = 5 s
VOI = 8 ml NT = 128
NAA
# of subject =1
Cr
PCr
Cr
PCr Glu
Gln Glu
Cho
Gln
Ins NAA
Ins Macromolecules
Tau Asp
Residual Water
5 4 3 2 1 ppm
18. LCModel Analysis
STEAM Tau
TE = 8 ms TR = 5 s Scyllo
VOI = 8 ml NT = 128 PE
NAAG
# of subject = 1
NAA
Lac
Measurement Ins
GSH
Glu
Gln
Glc
GABA
Fit Asp
Asc
Residual MAC
Baseline
4.0 3.5 3.0 2.5 2.0 1.5 1.0 4.0 3.5 3.0 2.5 2.0 1.5 1.0 ppm
19. Neurochemical Profile in the Occipital
Cortex at 7T
STEAM
TE = 8 ms TR = 5 s
VOI = 8 ml NT = 128
# of subjects = 5
15
Error bars = SD
10
µmol/g
5
0
NAA+NAAG
GABA
NAA
NAAG
Scyllo
Ins
Lac
Gln
Glu
GPC+PCho
PE
Cr
PCr
GSH
Glc+Tau
20. 1H Spectrum of Posterior Cingulate at
7T
STEAM
TE = 8 ms TR = 5 s NAA
VOI = 8 ml NT = 128
# of subject =1
Cr
PCr
Cr
Cho
PCr Glu Glu
Residual Water
Gln
Ins
Gln
Ins NAA Macromolecules
Tau
5 4 3 2 1 ppm
21. 1H Spectrum of Frontal White Matter at
7T
STEAM
TE = 8 ms TR = 5 s NAA
VOI = 8 ml NT = 128
# of subject =1
Cr
PCr
Cho
Cr
PCr
Glu
Residual Water
Gln Glu
Ins
Gln
Ins Macromolecules
Tau NAA
5 4 3 2 1 ppm
22. 1H Spectrum of Putamen at 7T
STEAM
TE = 8 ms TR = 5 s
VOI = 2.5 ml NT = 256
# of subject =1
NAA
Cr
PCr
Cr
Cho
PCr Glu
Glu
Gln
Ins Gln
Ins Tau NAA Macromolecules
Residual Water
5 4 3 2 1 ppm
23. 1H Spectrum of Substantia Nigra at 7T
STEAM
TE = 8 ms TR = 5 s
VOI = 1 ml NT = 512
# of subject =1
NAA
Cr
PCr
Cho
Cr Glu
PCrGlu Gln
NAA Macromolecules
Gln Ins
Ins
5 4 3 2 1 ppm
24. 1H Spectrum of Pons at 7T
STEAM
TE = 8 ms TR = 5 s
VOI = 4 ml NT = 128
# of subject =1
NAA
Cho
Cr
PCr
Cr
PCr
Glu Glu
Gln Ins Gln Macromolecules
Tau NAA
Residual Water Ins
5 4 3 2 1 ppm
25. 1H Spectrum of Cerebellar Vermis at
7T
STEAM
TE = 8 ms TR = 5 s
VOI = 6.25 ml NT = 128
# of subject =1
Cr
NAA
PCr
Cho
Cr
PCr Glu
Glu
Gln Ins Gln
Ins NAA Macromolecules
Tau
Residual Water
5 4 3 2 1 ppm
26. 1H Spectrum of Pons at 7T
STEAM
TE = 8 ms TR = 5 s
VOI = 4 ml NT = 128
# of subject =1
NAA
Cho
Cr
PCr
Cr
PCr
Glu Glu
Gln Ins Gln Macromolecules
Tau NAA
Residual Water Ins
5 4 3 2 1 ppm
27. Neurochemical Profiles
Posterior Putamen
Cingulate VOI = 2.5 ml
VOI = 8 ml # of subjects = 5
# of subjects = 5
Frontal White Matter Substantia Nigra
VOI = 8 ml VOI = 1 ml
# of subjects = 5 # of subjects = 5
29. Summary
Multiple brain regions-of-interest to
study several neurological diseases
– Multi channel coil
– B1 Shimming
Reliable quantification of 10-15
metabolites
Increased SNR
Implemented on a clinical console
