L S
N I
Charles L. Epstein, PhD
Departments of Mathematics and
Radiology
University of Pennsylvania
*
This lecture is dedicated to the memory of
NMR pioneer, and my former Penn colleague
Jack Leigh, 1939-2008

Speaker Name:Charles L. Epstein
I am the author of an “Introduction to the Mathematics of Medical
Imaging,” published by SIAM Press, which bears some relationship to the topic of this talk.

RF-pulse design
In essentially every application of
NMR, one needs to selectively excite spins, and this requires the design of an RF-pulse envelope.
We discuss the problem of designing an
RF-envelope to attain a specified transverse magnetization, as a function of the offset frequency, for a single species of spins, assuming that there is no relaxation.

The message of this talk is that this problem, which has retained a certain mystique among MR-physicists, has an exact solution, with efficient numerical implementations, not much harder than the Fast Fourier Transform.

Outline
1.
The Bloch Equation
2.
Non-selective pulses
3.
The problem of selective pulse design
4.
Small flip angle pulses: the Fourier method
5.
Large flip angle pulses
I.
The Spin Domain Bloch Equation (SBDE)
II.
Scattering and and Inverse Scattering for the
SBDE
III.
Selective pulse design and the Inverse
Scattering Transform
6.
The hard pulse approximation
7.
SLR
8.
DIST
9.
Examples

The Bloch equation
In MRI, the process of RF-pulse design begins with a single mathematical model, the Bloch Phenomenological Equation, without relaxation: dM dt
  B  M bulk magnetization produced by the denotes the applied magnetic induction field.

The B-Field
The Bfield has three constituent parts:
B  B
0
 B
1
( t )  G ( r , t ).
We choose coordinates so that B
0
=(0,0,b
0
).
B
1
( t )  ( e i 
0 t ( 
1
( t )  i 
2
( t )),0)
We write vectors in R 3
, as a complex number, paired with a real number,
(a+ib,c).

0
  b
0 is the Larmor frequency defined by the background field.

The gradient fields
The gradients are quasi-static fields,
G(r,t), which produce a spatial variation in the Larmor frequency. We use f to denote the offset frequency, or local change in the Larmor frequency.

The Rotating Reference Frame
In the sequel, we work with in the rotating reference defined by B
0
. The magnetization in this frame, m(t), is given by:

 m x m y
( t ) 
( t )
 m z
( t )




 cos
 sin
0


0 t sin
0 t cos


0
0 t 0 t 0
0 1






M
M x y
( t ) 
( t )

.
M z
( t )

The Bloch Equation in the Rotating Frame
The vector m(t) satisfies the differential equation: d dt 


 m x m y m z
(
( t ) 
( t )
 t )






 f
0
 
2
( t )
 f

0 
1
( t )


0
2
1
( t )
( t )







 m m y m x z
( t ) 
( t )

( t ) 

.
Recall that f is the offset frequency, and the RF-envelope, is the B
1 field in the rotating frame.

The problem of selective pulse design reduces to that of understanding how solutions to the Bloch equation depends on the the B
1
-field. That is, how does m(f;t) depend on
(

1
( t )
 i

2
( t ), 0)?
While, this dependence is nonlinear, it can still be understood in great detail.

Linear versus non-linear dependence

Non-selective pulses
The easiest case to analyze is when there is no gradient field, (so f=0 ) and the B
1
-field is aligned along a fixed axis:
B
1
( t )  ( 
1
( t ),0,0).
In this situation the excitation is nonselective. Starting from equilibrium m(0)=(0,0,1), at time t the magnetization is rotated about the x -axis through an
 ( t )

The flip angle: t
 ( t )  
0

1
( s ) ds

Selective pulse design
In the basic problem of selective RFpulse design:
The data is : a target magnetization profile: m tar (f)=(m tar x
(f), m tar y
(f), m tar z
(f)).
The goal: To find an RF-envelope:
( 
1
( t )  i 
2
( t ),0) non-zero in [0,T] so that at time T :

 m m y x
(
( f ; T )  f ; T )
 m z f ;( T )




 cos sin
0


0
0
  sin 
 cos
0

0
0

 0
0
1





 m x tar m tar y
( f ) 
( f )

.
m z tar ( f ) 

As noted above, the map from the RFenvelope to m(f;T) is non-linear, so the problem of RFpulse design is as well.

The small flip angle approximation
While the general pulse design problem is non-linear, so long as the maximum desired flip angle is “small”, a very simple linear approximation suffices: d ( m
1
( f ; t )  im
2
( f ; t )) dt
 if ( m
1
( f ; t )  im
2
( f ; t ))  i  ( 
1
( t )  i 
2
( t ))
Starting at equilibrium, the solution at time T is: m
1
( f ; T )  im
2
( f ; T )   ie ifT 
T
0
 ( 
1
( s )  i 
2
( s )) e  isf ds

 ( t )   i  (  ( t )  i  the solution at time T , is easily expressed in terms of the Fourier transform of
  m x
( f ; T )  im y
( f ; T )  e ifT

 ( f )
Since we want m(f;T) m tar ( f )  im tar to be , applying the inverse Fourier transform we find that:
 ( t ) 
1
2 


 ( m x tar ( f )  im tar y
( f )) e ift dt

Small flip angle examples
We illustrate the Fourier method by designing pulses intended to excite a window of width 2000Hz with transition regions of 200Hz on either side. Below are pulses with flip angles 30

, 90

, and 140

, and the transverse components they produce starting from equilibrium.

30

Fourier pulse

90

Fourier pulse

140

Fourier pulse

2D, 3D, Spatial-Spectral-Pulses
The small flip angle approximation can also be used to design 2D, 3D and spatial-spectral pulses. One combines varying gradients, and the formalism of excitation k-space to interpret the solution of the linearized Bloch equation as an approximation to a higher dimensional Fourier transform.

General properties of pulses
1. Sharp transitions in the pulse envelope produce “ringing” in the magnetization profile
2. A longer pulse is needed to produce a sharp transition in the magnetization profile.
3. Shifting an envelope in time leads to a linear phase change in the profile.

“Large” flip angle pulses
The Spin Domain Bloch Equation
The starting point for direct, large flip angle pulse design is the Spin Domain
Bloch Equation (SDBE). The SDBE is related, in a simple way, to the Bloch equation for the magnetization, which is the quantum mechanical observable.

The spinor representation
We represent the spin state as a pair of complex numbers
  ( 
1
, 
2
) such that
| 
1
| 2  | 
2
| 2  1
It is related to the magnetization by m  ( m x
 im y
, m z
)  (2 
1
* 
2
,| 
1
| 2  | 
2
| 2 )

Spin Domain Bloch Equation

 .
It solves the Spin Domain Bloch
Equation: d 
(  ; t )  dt


 i  q ( t ) 
 q * ( t ) i  
 (  ; t )
Where q ( t ) 

2
( 
2
( t )  i 
1
( t )).
We call q(t) the “potential function”.

Scattering theory for the SDBE
In applications to NMR, the q ( t )
For t<t
0
,
[ t , t ]
 (  ; t )  ( e  i  t ,0) is a solution to the SDBE, representing the equilibrium state. There are t  t
( a (  ), b (  ))

1 
(  ; t )  ( a (  ) e  i  t , b (  ) e i  t ).

Scattering data and the target profile
The functions, a and b are called the scattering coefficients.If m(f;t) is the corresponding solution of the Bloch
Equation, then, for t>t
1
, we have the fundamental relation: r (  )  b ( a (

 )
)
 m x
(2  ; t )
1  m z
 im
(2 y
(2
 ; t )
 ; t ) e  2 i  t e  2 i  t
The exponential is connected to rephasing.

The right hand side does not depend r (  ) the reflection coefficient . To define a selective excitation we specify a target magnetization profile. This is equivalent to specifying a reflection coefficient: r


2 f


 m x tar ( f )  im
1  m z tar tar y
( f )
( f )
.

Parseval relation
The potential q(t) and the scattering coefficient r(

) are like a Fourier transform pair. They satisfy a nonlinear Parseval relation:
 | 
1
( t )  i 
2
( t ) | 2 dt 
2
 2
 
Power  W log

 1
180   

 | r (  ) | 2
 d 

Pulse design and Inverse Scattering m tar (f)
( 
1
( t )  i 
2
( t ),0)
Stereographic projection r (  ) q ( t )
IST

Truth in advertising
The inverse scattering problem has optional auxiliary parameters, called bound states. This means that there are infinitely many different solutions to any pulse design problem. If no auxiliary parameters are specified, then one obtains the minimum energy solution.
No more will be said about this topic today
.

The classical IST
The inverse scattering transform finds q(t) given r(

) . To find q(t) , for each t , we can solve an integral equation of the form: k t

( s )  F ( t , x ) k t t
 ( x ) dx  g ( t  s )
The potential is found from: q ( t )   2 k t
( t ).

The hard pulse approximation
We model the RF-pulse envelope as a sum of equally spaced Dirac delta functions
: q h
( t )    j
 ( t  j  )
The Shinnar-Leigh-Le Roux (SLR) method of pulse design makes essential use of the SPDE and the hard pulse approximation.

Hard Pulse Recursion Equation
A limiting solution to the SDBE has jumps at the times { j

}, and freely precesses in the gradient field between the jumps. At the jumps we have a simple recursion relation ( HPRE ):
 A j  1
( w ) 
 B j  1
( w ) 



 w  j j
  j
* w  j


 A j
( w ) 
 B j
( w ) 
Where: w  e 2 i    j

|
 j
*
 j
|
1  cos |  j
2
|
 j
 1  |  j
| 2

Scattering theory for the Hard Pulse Recursion
Equation the recursion that tends to (1,0) as the index then the reflection coefficient , R(w), is the limit:
R ( w )  lim j   w  j B j 
( w )
A j 
( w )

If we choose the spacing
 sufficiently small, then this function is related to the target magnetization profile by:
R ( w )  r

 log w
2 i 



Inverse scattering for the HPRE and pulse design
The pulse design problem is now reduced to solving the inverse scattering problem for the HPRE:
Find a sequence of coefficients
 j
 so that the reflection coefficient is a good approximation to that defined by the target magnetization profile
.
SLR and DIST can be used to solve this problem

SLR as an inverse scattering algorithm
First we find polynomials , (A(w),B(w)), so that the ratio B(w)/A(w) is, in some sense, an approximation to R(w).
In most implementations of SLR, one first chooses a polynomial B(w), so that
|B(w)| 2 is a good approximation to:
| R ( w ) | 2
1  | R ( w ) | 2

Note that the flip angle is:

( w )

2sin

1
(| B ( w ) |)
A polynomial A(w) is then determined using the relation:
| A ( w ) | 2  | B ( w ) | 2  1 for |w|=1 .
The phase of B(w) is then selected using standard filter design tools.

SLR schematic
R(w)
Polynomial design
|B(w)|
Hilbert transform
(A(w),B(w))
Inverse SLR
{  j
}

A limitation of this approach is that the phase of the magnetization profile is not specified, but is “recovered” in the process of finding the polynomial A(w) and the choice of phase for B(w) .
On the other hand, the duration of the pulse is specified, in advance by the choice of
 and the degree of the polynomial B(w) .

The Discrete Inverse Scattering Transform
(DIST)
The DIST is another approach to solving the inverse scattering problem for the (HPRE). With DIST we directly approximate R(w):
R
D
( w ) 
N
1  j  M r j w j
The upper limit N
1
, specifies the rephasing time to be N
1

.
( A
N
1

( w ), B
N
1

( w ))  (1,0)

R(w)
“Polynomial” design
R app
(w)
DIST transform
(A j
(w),B j
(w);
{  j
}
)

•The DIST algorithm provides direct control on the phase, flip angle and rephasing time.
•It sacrifices direct control on the duration of the pulse.
•Both algorithms have an approximation step and a recursion step.
•The recursion steps have a computational complexity similar to that of the
Fast Fourier Transform.

These pulses are designed with the indicated algorithms to produce flip angle 140
 in a 2kHz window, with a .2 kHz transition band on either side. The nominal rephasing time is 5ms.

SLR
140
 pulses
DIST

SLR DIST

SLR DIST

Start band End band Flip angle
-1 kHz 1 kHz 140 degrees
Phase
0 degrees
1 kHz 3 kHz 90 degrees 60 degrees
SLR DIST

• Thanks to my collaborator Jeremy
Magland for his help understanding this subject and for creating MR pulsetool.
• Thanks to Felix Wehrli and LSNI.
• Research partially supported by*
• NIH R01-AR050068, R01-AR053156
• DARPA: HR00110510057
• NSF: DMS06-03973
*Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the NIH,
NSF, or DARPA.