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In vivo MRI of Fast Relaxing Spins Using a Swept Radiofrequency Djaudat Idiyatullin, Curt Corum, Jang-Yeon Park, Michael Garwood Center for Magnetic Resonance.

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Presentation on theme: "In vivo MRI of Fast Relaxing Spins Using a Swept Radiofrequency Djaudat Idiyatullin, Curt Corum, Jang-Yeon Park, Michael Garwood Center for Magnetic Resonance."— Presentation transcript:

1 In vivo MRI of Fast Relaxing Spins Using a Swept Radiofrequency Djaudat Idiyatullin, Curt Corum, Jang-Yeon Park, Michael Garwood Center for Magnetic Resonance Research, Cancer Center, and Department of Radiology, University of Minnesota

2 What means the “fast relaxing spins”? Range of relaxation times (T 2 ): 10 -3 – 10 -5 s Dark zone for regular imaging sequences (gradient echo) Imaging of fast relaxing spins T 2 ~ t 90 Echo, slice selection → impractical Excitation → acquisition (FID)

3 Imaging of fast relaxing spins G 11 acq BLAST (Back-projection Low Angle Shot Technique) Sensitivity, contrast: low Problems: first points in k-space, distortion the excitation profile in image space

4 Imaging of fast relaxing spins G 11 acq 11 G  RF -  c Amplitude and frequency modulated pulses Hyperbolic secant (HSn) pulses low peak power to excite a large bandwidth, flat excitation profile BLAST (Back-projection Low Angle Shot Technique) Sensitivity, contrast: low Problems: first points in k-space, distortion the excitation profile in image space

5 11 G  RF -  c acq Imaging of fast relaxing spins G 11 acq BLAST (Back-projection Low Angle Shot Technique) Sensitivity, contrast: low Problems: first points in k-space, distortion the excitation profile in image space Amplitude and frequency modulated pulses Hyperbolic secant (HSn) pulses low peak power to excite a large bandwidth, flat excitation profile Interleaved excitation and sampling Sensitive to spins with a very short T2 energy of the signal distributed in time Problem: it is not a regular FID spins + sweep excitation (< 90 o ) → linear system

6 Correlation method for linear system Response Excitation, x(t) Spin system, h(t) System spectrum Conjugate multiplication FT

7 Correlation method for linear system Simulated data for HS4 pulse Response Excitation, x(t) Spin system, h(t) System spectrum Conjugate multiplication FT

8 SWeep Imaging with Fourier Transform (SWIFT) HSn pulses Flip angle < 90 degree T R ~ T p Bw=sw=2πN/T p Projection reconstruction

9 Slices of 3D image of the feet sw = 20kHz 128 x 64 x64 4T First in vivo SWIFT 3D image

10 MIP of 3D images of empty 16-element TEM head coil sw = 32kHz 128x128 x 64 4T Sensitivity to short T 2

11 MIP of 3D images of plastic toy in the breast coil sw = 39kHz 128x128 x 128 D=25cm 4T The breast coil’s building material has T 2 ~ 0.3 ms. Sensitivity to short T 2

12 Selected slices of 3D images of a normal mandible and surrounding areas in a 48-year-old man (4T). SWIFT sw = 62 kHz, 256 x 128 x 64 Gradient-echo sw = 80 kHz, T E = 3ms 256 x 256 x 64

13 Direct MRI of the teeth 3D MRI of decayed molar tooth obtained with SWIFT (10 min) sw = 62 kHz, 4.7T demineralization pulp cementum plaque root dentin

14 Conclusions (a)fast; The method avoids the delays and gradient switching, and also time for an excitation pulse (it’s combined with the acquisition period). (b) sensitive to short T2 ; any T 2 > 1/sw. (c) reduced motion artifacts; Because the SWIFT method has no “echo time” it is expected to be less sensitive to motion and flow artifacts than conventional MRI methods. (d) reduced signal dynamic range; The different frequencies are excited sequentially the resulting signal is distributed in time, leading to a decreased amplitude of the acquired signal. This allows more effective utilization of the dynamic range of the digitizer. (e) quiet. The SWIFT method uses a small step when changing gradients between projections, and the fast gradient switching that creates loud noise can be avoided.

15 Fast and quiet MRI using a swept radiofrequency Djaudat Idiyatullin, Curt Corum, Jang-Yeon Park, Michael Garwood Journal of Magnetic Resonance 181 (2006), available online Acknowledgments. This research was supported by NIH grants RR008079 and CA92004. The authors would like to thank Dr. Ivan Tkac for helping with reconstruction software implementation and Dr. Jutta Ellermann for assistance in conducting the experiments


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