Download presentation

Presentation is loading. Please wait.

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**

w q = wt The spatial information of the proton pools contributing MR signal is determined by the spatial frequency and phase of their magnetization.

5
Gradient Coils z z z y y y x x x X gradient Y gradient Z gradient 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.

6
**A Simple Example of Spatial Encoding**

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

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 Mxy 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 Mxy in a Thin Slice of Tissue**

Excite: Source of RF frequency on resonance Addition of small frequency variation Amplitude modulation with “sinc” function RF power amplifier RF coil

10
**Electromagnetic Excitation Pulse (RF Pulse)**

Fo FT t Fo Fo+1/ t Time Frequency Fo Fo FT DF= 1/ t t

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. Center frequency [63 MHz at 1.5 T] f 60 KHz Gx = 1 Gauss/cm = 10 mTesla/m = strength of gradient field x-axis Left = –7 cm Right = +7 cm

12
**Exciting and Receiving Mxy in a Thin Slice of Tissue**

Receive: RF coil RF preamplifier Filters Analog-to-Digital Converter Computer memory

13
Slice Selection

14
**Slice Selection – along z**

15
**Determining slice thickness**

Resonance frequency range as the result of slice-selective gradient: DF = gH * Gsl * dsl The bandwidth of the RF excitation pulse: Dw/2p Thus the slice thickness can be derived as dsl = Dw / (gH * Gsl * 2p)

16
**Changing slice thickness**

There are two ways to do this: Change the slope of the slice selection gradient 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: Change the position of the zero point of the slice selection gradient with respect to isocenter (b) Change the center frequency of the RF to correspond to a resonance frequency at the desired slice F = gH (Bo + Gsl * Lsl ) 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 (B1) 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 2T2*, 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**

Constant Magnetic Field Varying Magnetic Field w/o encoding w/ encoding

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
**After Frequency Encoding**

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

26
**A typical diagram for MRI frequency encoding: Gradient-echo imaging**

Excitation Slice Selection TE Frequency Encoding readout ……… Time point #1 Time point #9 Readout Data points collected during this period corrspond to one-line in k-space

27
**……… Phase Evolution of MR Data TE Gradient Phases of spins**

digitizer on Phases of spins Gradient TE ……… Time point #1 Time point #9

28
**A typical diagram for MRI frequency encoding:**

Spin-echo imaging Excitation Slice Selection TE Frequency Encoding readout ……… Readout

29
Phase History 180o TE Phase 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(ft) and cos([f+f]t) will be more different

31
**Calculation of the Field of View (FOV) along frequency encoding direction**

* Gf * FOVf = BW = 1/Dt Which means FOVf = 1/ (g Gf Dt) 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 Mxy (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
**After Frequency Encoding**

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

34
**A typical diagram for MRI phase encoding: Gradient-echo imaging**

readout Excitation Slice Selection Frequency Encoding Phase Readout ………

35
**A typical diagram for MRI phase encoding:**

Spin-echo imaging readout Excitation Slice Selection Frequency Encoding Phase Readout ………

36
**Calculation of the Field of View (FOV) along phase encoding direction**

* Gp * FOVp = Np / Tp Which means FOVp = 1/ (g Gp Tp/Np) = 1/ (g Gp Dt) where Tp is the duration and Np 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
**…….. …….. …….. …….. Phase Encode Time Time Time Step 1 point #1**

39
. +Gx -Gx +Gy -Gy Physical Space K-Space 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 Kx = g/2p 0t Gx(t) dt Ky = g/2p 0t Gy(t) dt**

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 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. Kx = g/2p 0t Gx(t) dt Ky = g/2p 0t Gy(t) dt

41
**Two Spaces k-space Image space ky y IFT kx x FT Final Image**

Acquired Data Image space x y Final Image IFT FT

42
Image K High Signal

43
**Full k-space Lower k-space Higher k-space Full Image**

Intensity-Heavy Image Detail-Heavy Image

44
**The k-space Trajectory**

Equations that govern k-space trajectory: Kx = g/2p 0t Gx(t) dt Ky = g/2p 0t Gy(t) dt time t Gx (amplitude) Kx (area)

45
**A typical diagram for MRI frequency encoding:**

A k-space perspective 90o Excitation Slice Selection Frequency Encoding readout Readout Exercise drawing its k-space representation

46
**The k-space Trajectory**

47
**A typical diagram for MRI frequency encoding:**

A k-space perspective 90o 180o Excitation Slice Selection Frequency Encoding readout Readout Exercise drawing its k-space representation

48
**The k-space Trajectory**

49
**A typical diagram for MRI phase encoding:**

A k-space perspective 90o Excitation Slice Selection Frequency Encoding Phase Encoding readout Readout Exercise drawing its k-space representation

50
**The k-space Trajectory**

51
**A typical diagram for MRI phase encoding:**

A k-space perspective 180o 90o Excitation Slice Selection Frequency Encoding Phase Encoding readout Readout Exercise drawing its k-space representation

52
**The k-space Trajectory**

53
Sampling in k-space Dk = gGDt kmax Dk = 1 / FOV

55
**. . . . . . . . . . . . . . . A B FOV: 10 cm Pixel Size: 2 cm**

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

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

58
**K-space can also help explain imaging distortions:**

Original image K-space trajectory Distorted Image

Similar presentations

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google