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Principles of MRI: Image Formation Allen W. Song Brain Imaging and Analysis Center Duke University.

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Presentation on theme: "Principles of MRI: Image Formation Allen W. Song Brain Imaging and Analysis Center Duke University."— Presentation transcript:

1 Principles of MRI: Image Formation Allen W. Song Brain Imaging and Analysis Center Duke University

2 What is image formation? To define the spatial location of the sources that contribute to the detected signal.

3 But MRI does not use projection, reflection, or refraction mechanisms commonly used in optical imaging methods to form image. So how are the MR images formed?

4 Frequency and Phase Are Our Friends in MR Imaging   =  t The spatial information of the proton pools contributing MR signal is determined by the spatial frequency and phase of their magnetization.

5 Gradient Coils Gradient coils generate spatially varying magnetic field so that spins at different location precess at frequencies unique to their location, allowing us to reconstruct 2D or 3D images. X gradient Y gradient Z gradient x y z x zz x yy

6 A Simple Example of Spatial Encoding 0.8 w/o encoding w/ encoding Constant Magnetic Field Varying Magnetic Field

7 Spatial Decoding of the MR Signal Frequency Decomposition

8 Steps in 3D Localization  Can only detect total RF signal from inside the “RF coil” (the detecting antenna)  Excite and receive M xy in a thin (2D) slice of the subject  The RF signal we detect must come from this slice  Reduce dimension from 3D down to 2D  Deliberately make magnetic field strength B depend on location within slice  Frequency of RF signal will depend on where it comes from  Breaking total signal into frequency components will provide more localization information  Make RF signal phase depend on location within slice

9  Exciting and Receiving M xy in a Thin Slice of Tissue Source of RF frequency on resonance Addition of small frequency variation Amplitude modulation with “sinc” function RF power amplifier RF coil Excite:

10 Electromagnetic Excitation Pulse (RF Pulse) 0t Fo Fo Fo+1/ t TimeFrequency t Fo Fo  F= 1/ t FT FT

11 Gradient Fields: Spatially Nonuniform B:  During readout (image acquisition) period, turning on gradient field is called frequency encoding --- using a deliberately applied nonuniform field to make the precession frequency depend on location  Before readout (image acquisition) period, turning on gradient field is called phase encoding --- during the readout (image acquisition) period, the effect of gradient field is no longer time-varying, rather it is a fixed phase accumulation determined by the amplitude and duration of the phase encoding gradient. x-axis f 60 KHz Left = –7 cmRight = +7 cm G x = 1 Gauss/cm = 10 mTesla/m = strength of gradient field Center frequency [63 MHz at 1.5 T]

12 Exciting and Receiving M xy in a Thin Slice of Tissue RF coil RF preamplifier Filters Analog-to-Digital Converter Computer memory Receive:

13 Slice Selection

14 Slice Selection – along z z

15 Determining slice thickness Resonance frequency range as the result of slice-selective gradient:  F =  H * G sl * d sl  F =  H * G sl * d sl The bandwidth of the RF excitation pulse:   Thus the slice thickness can be derived as d sl =  / (  H * G sl * 2  d sl =  / (  H * G sl * 2 

16 Changing slice thickness There are two ways to do this: (a)Change the slope of the slice selection gradient (b)Change the bandwidth of the RF excitation pulse Both are used in practice, with (a) being more popular

17 Changing slice thickness new slice thickness

18 Selecting different slices In theory, there are two ways to select different slices: (a)Change the position of the zero point of the slice selection gradient with respect to isocenter selection gradient with respect to isocenter (b) Change the center frequency of the RF to correspond to a resonance frequency at the desired slice to a resonance frequency at the desired slice F =  H (Bo + G sl * L sl ) Option (b) is usually used as it is not easy to change the isocenter of a given gradient coil.

19 Selecting different slices new slice location

20  Readout Localization (frequency encoding)  After RF pulse (B 1 ) ends, acquisition (readout) of NMR RF signal begins  During readout, gradient field perpendicular to slice selection gradient is turned on  Signal is sampled about once every few microseconds, digitized, and stored in a computer Readout window ranges from 5–100 milliseconds (can’t be longer than about 2  T2*, since signal dies away after that)  Computer breaks measured signal V(t) into frequency components v(f ) — using the Fourier transform  Since frequency f varies across subject in a known way, we can assign each component v(f ) to the place it comes from

21 Spatial Encoding of the MR Signal w/o encoding w/ encoding Constant Magnetic Field Varying Magnetic Field

22 It’d be easy if we image with only 2 voxels … But often times we have imaging matrix at 256 or higher.

23 More Complex Spatial Encoding x gradient

24 More Complex Spatial Encoding y gradient

25 Physical Space A 9×9 case Before Encoding After Frequency Encoding (x gradient) So each data point contains information from all the voxels MR data space

26 A typical diagram for MRI frequency encoding: Gradient-echo imaging readout Excitation Slice SliceSelection Frequency Encoding Encoding Readout TE Data points collected during this period corrspond to one-line in k-space ……… Time point #1 Time point #9

27 Phase Evolution of MR Data digitizer on Phases of spins GradientTE……… Time point #1 Time point #9

28 A typical diagram for MRI frequency encoding: Spin-echo imaging readout Excitation Slice SliceSelection Frequency Encoding Encoding Readout TE ………

29 Phase History 180 o TEPhase Gradient ……… digitizer on

30 Image Resolution (in Plane)  Spatial resolution depends on how well we can separate frequencies in the data V(t)  Resolution is proportional to  f = frequency accuracy  Stronger gradients  nearby positions are better separated in frequencies  resolution can be higher for fixed  f  Longer readout times  can separate nearby frequencies better in V(t) because phases of cos(f  t) and cos([f+  f]  t) will be more different

31 Calculation of the Field of View (FOV) along frequency encoding direction  * G f * FOV f = BW = 1/  t Which means FOV f = 1/ (  G f  t) where BW is the bandwidth for the receiver digitizer.

32  The Second Dimension: Phase Encoding  Slice excitation provides one localization dimension  Frequency encoding provides second dimension  The third dimension is provided by phase encoding:  We make the phase of M xy (its angle in the xy-plane) signal depend on location in the third direction  This is done by applying a gradient field in the third direction (  to both slice select and frequency encode)  Fourier transform measures phase  of each v(f ) component of V(t), as well as the frequency f  By collecting data with many different amounts of phase encoding strength, can break each v(f ) into phase components, and so assign them to spatial locations in 3D

33 Physical Space A 9×9 case Before Encoding After Frequency Encoding x gradient After Phase Encoding y gradient So each point contains information from all the voxels MR data space

34 A typical diagram for MRI phase encoding: Gradient-echo imaging readout Excitation Slice SliceSelection Frequency Encoding Encoding Phase Phase Encoding Encoding Readout ………

35 A typical diagram for MRI phase encoding: Spin-echo imaging readout Excitation Slice SliceSelection Frequency Encoding Encoding Phase Phase Encoding Encoding Readout ………

36 Calculation of the Field of View (FOV) along phase encoding direction  * G p * FOV p = N p / T p Which means FOV p = 1/ (  G p T p /N p ) = 1/ (  G p  t) = 1/ (  G p  t) where T p is the duration and N p the number of the phase encoding gradients, Gp is the maximum amplitude of the phase encoding gradient.

37 Part II.2 Introduction to k-space (MR data space) Image k-space

38 PhaseEncode Step 1 PhaseEncode Step 2 PhaseEncode Step 3 Time point #1 Time point #2 Time point #3 …….. Time point #1 Time point #2 Time point #3 …….. Time point #1 Time point #2 Time point #3 …….. ……..

39 Contributions of different image locations to the raw k-space data. Each data point in k-space (shown in yellow) consists of the summation of MR signal from all voxels in image space under corresponding gradient fields.

40 Acquired MR Signal From this equation, it can be seen that the acquired MR signal, which is also in a 2-D space (with kx, ky coordinates), is the Fourier Transform of the imaged object. For a given data point in k-space, say (kx, ky), its signal S(kx, ky) is the sum of all the little signal from each voxel I(x,y) in the physical space, under the gradient field at that particular moment Kx =  /2  0 t  Gx(t) dt Ky =  /2  0 t  Gy(t) dt

41 Two Spaces FT IFTk-space kxkxkxkx kykykyky Acquired Data Image space x y Final Image

42 Image K HighSignal

43 Full k-space Lower k-space Higher k-space Full Image Intensity-Heavy Image Detail-Heavy Image

44 The k-space Trajectory Kx =  /2  0 t  Gx(t) dt Ky =  /2  0 t  Gy(t) dt Equations that govern k-space trajectory: time 0t Gx (amplitude) Kx (area)

45 A typical diagram for MRI frequency encoding: A k-space perspective readout Excitation Slice SliceSelection Frequency Encoding Encoding Readout Exercise drawing its k-space representation 90 o

46 The k-space Trajectory

47 A typical diagram for MRI frequency encoding: A k-space perspective readout Excitation Slice SliceSelection Frequency Encoding Encoding Readout Exercise drawing its k-space representation 90 o 180 o

48 The k-space Trajectory

49 A typical diagram for MRI phase encoding: A k-space perspective readout Excitation Slice SliceSelection Frequency Encoding Encoding Phase Phase Encoding Encoding Readout Exercise drawing its k-space representation 90 o

50 The k-space Trajectory

51 A typical diagram for MRI phase encoding: A k-space perspective readout Excitation Slice SliceSelection Frequency Encoding Encoding Phase Phase Encoding Encoding Readout Exercise drawing its k-space representation 90 o 180 o

52 The k-space Trajectory

53 Sampling in k-space k max  k =  G  t  k = 1 / FOV

54

55 A B FOV: 10 cm Pixel Size: 1 cm FOV: Pixel Size: 10 cm 2 cm 2 cm

56 A B FOV: 10 cm Pixel Size: 1 cm FOV: Pixel Size: 5 cm 1 cm

57 A B FOV: 10 cm Pixel Size: 1 cm FOV: Pixel Size: 20 cm 2 cm 2 cm

58 Original imageK-space trajectoryDistorted Image K-space can also help explain imaging distortions:


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