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[1]	
  Stark	
  DD.	
  Radiology,	
  1991;	
  179:333–5.	
  
[2]	
  Anderson	
  LJ,	
  et.	
  al.	
  Eur	
  Heart	
  J.,	
  2001;	
  22(23):2171-­‐9.	
  
[3]	
  Kellman	
  PK,	
  et.	
  al.	
  J	
  Cardiovasc	
  Magn	
  Reson.,	
  2013;	
  15(1):
56.	
  
The	
  esSmate	
  SD’s	
  turned	
  out	
  to	
  be	
  significantly	
  close	
  to	
  the	
  measured	
  
SD’s.	
  Below	
  are	
  the	
  acquired	
  phantom	
  images	
  and	
  their	
  associated	
  T2*,	
  
measured	
  SD,	
  and	
  esSmated	
  SD	
  maps.	
  
	
  
	
  
	
  
	
  
Reliable	
  idenSficaSon	
  of	
  T2*	
  
abnormaliSes	
  is	
  limited	
  by	
  	
  
measurement	
  noise	
  which	
  	
  
Reduces	
  the	
  precision	
  of	
  T2*	
  	
  
esSmaSon.	
  Knowing	
  how	
  precise	
  	
  
a	
  T2*	
  measurement	
  is	
  important	
  	
  
for	
  prototyping	
  new	
  techniques	
  or	
  	
  
if	
  a	
  clinician	
  is	
  deciding	
  how	
  	
  
aggressively	
  to	
  treat	
  a	
  paSent	
  	
  
with	
  a	
  border	
  line	
  case.	
  We	
  	
  
propose	
  a	
  method	
  for	
  	
  
evaluaSng	
  the	
  precision	
  of	
  T2*	
  	
  
mapping	
  techniques	
  by	
  esSmaSng	
  	
  
the	
  standard	
  deviaSon	
  (SD)	
  of	
  
each	
  measurement	
  thereby	
  
creaSng	
  an	
  SD	
  map.	
  
Evaluating precision of T2* mapping methods
for MRI tissue characterization
Christopher Sandino1, Hui Xue2, Peter Kellman2
	
  
	
  
The	
  standard	
  deviaSon	
  for	
  each	
  T2*	
  can	
  be	
  esSmated	
  by	
  esSmaSng	
  the	
  
parameters’	
  (A,	
  T2*)	
  covariance	
  matrix	
  [3]:	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
All	
  methods	
  were	
  implemented	
  in	
  C++	
  so	
  that	
  they	
  can	
  be	
  tested	
  and	
  ran	
  
on	
  an	
  MRI	
  scanner	
  using	
  medical	
  image	
  reconstrucSon	
  framework	
  
Gadgetron.	
  
	
  
Experimental	
  Valida/on	
  
In	
  order	
  to	
  validate	
  the	
  SD	
  map	
  formulaSon,	
  	
  
we	
  made	
  six	
  gel	
  phantoms	
  made	
  of	
  50	
  mL	
  	
  
soluSons	
  of	
  1.5%	
  agarose	
  gel,	
  cupric	
  sulfate	
  	
  
(CuSO4),	
  saline	
  and	
  varying	
  concentraSons	
  of	
  	
  
Feridex	
  I.V.,	
  an	
  intravenous	
  contrast	
  agent	
  	
  
containing	
  colloids	
  of	
  iron	
  oxide.	
  The	
  	
  
phantoms	
  were	
  scanned	
  N=64	
  Smes	
  to	
  measure	
  the	
  “true”	
  SD	
  and	
  
compare	
  them	
  to	
  the	
  esSmated	
  SD	
  on	
  a	
  per-­‐pixel	
  basis.	
  
	
  
Measured	
  and	
  esSmated	
  	
  
SD’s	
  were	
  compared	
  for	
  
two	
  methods	
  of	
  T2*	
  	
  
mapping	
  (convenSonal	
  
and	
  truncated)	
  and	
  	
  
both	
  showed	
  >99%	
  	
  
correlaSon.	
  Note	
  that	
  	
  
higher	
  SDs	
  are	
  biased	
  	
  
upwards.	
  These	
  correspond	
  	
  
to	
  curves	
  with	
  longer	
  T2*	
  	
  
which	
  aren’t	
  sampled	
  at	
  long	
  enough	
  echo	
  Smes.	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
In-­‐vivo	
  studies	
  showed	
  relaSvely	
  high	
  SD	
  in	
  the	
  
bloodpool,	
  the	
  heart-­‐lung	
  interface,	
  and	
  areas	
  where	
  
moSon	
  occurred.	
  The	
  figure	
  above	
  shows	
  a	
  possible	
  
clinical	
  applicaSon	
  where	
  the	
  SD	
  map	
  is	
  used	
  to	
  mask	
  
unreliable	
  pixels.	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  	
  	
  	
  	
  T2*	
  mapping	
  is	
  a	
  widely	
  used,	
  non-­‐invasive	
  
magneSc	
  resonance	
  imaging	
  (MRI)	
  technique	
  used	
  to	
  
detect	
  iron	
  overload	
  in	
  Sssue.	
  High	
  concentraSons	
  of	
  
iron	
  change	
  the	
  paramagneSc	
  properSes	
  of	
  the	
  Sssue	
  
around	
  it,	
  including	
  its	
  T2*	
  -­‐	
  the	
  transverse	
  relaxaSon	
  
Sme	
  aler	
  an	
  electromagneSc	
  excitaSon	
  [1].	
  
	
  
	
  	
  
	
  
	
  
	
  
	
  
	
  
The	
  T2*	
  relaxaSon	
  curve	
  
is	
  measured	
  by	
  sampling	
  	
  
MR	
  signal	
  intensiSes	
  at	
  	
  
various	
  echo	
  Smes	
  (TE)	
  [2].	
  
We	
  know	
  that	
  this	
  curve	
  is	
  
of	
  the	
  form	
  	
  
Yi=A*exp(-­‐TEi/T2*),	
  so	
  we	
  	
  
can	
  use	
  an	
  exponenSal	
  	
  
regression	
  to	
  esSmate	
  T2*	
  
	
  for	
  each	
  pixel	
  thereby	
  	
  
creaSng	
  a	
  map	
  such	
  as	
  the	
  	
  
one	
  shown	
  to	
  the	
  right.	
  	
  
This	
  parScular	
  paSent	
  has	
  low	
  T2*	
  in	
  the	
  liver,	
  a	
  sign	
  
of	
  iron	
  overload	
  due	
  to	
  β-­‐thalassemia.	
  
Introduc/on	
  
Objec/ve	
  
Methodology	
  
0 5 10 15 20
0
1
2
3
4
5
6
7
8
Echo Time (ms)
SNR
Observed
Truth
Fitted
0 5 10 15 20
0
1
2
3
4
5
6
7
Echo Time (ms)
SNR
Observed
Truth
Fitted
Low	
  SNR	
  
Low	
  T2*	
  
y	
  =	
  1.0446x	
  -­‐	
  0.0004	
  
R²	
  =	
  0.99959	
  
0	
  
1	
  
2	
  
3	
  
0	
   1	
   2	
   3	
  
Es/mated	
  SD	
  (ms)	
  
Measured	
  SD	
  (ms)	
  
T2*
meas.
SD
meas.
SD
est.
time
(ms)
RF
1.6 3.9 6.2 8.5 10.8 13.2 15.5 17.8
Mul -echo phantom images
Observed
Non-Linear
Regression
Estimated
TE
y
TE
y'
TE
y-y'
Residuals
Robust
Estimation
of variance
med(abs(residuals))
Compute
Covariance
Matrix
Results	
  
Good	
  Breath-­‐hold	
  Poor	
  Breath-­‐hold	
  
T2*	
  map	
   SD	
  map	
   T2*	
  map	
  w/	
  mask	
  
	
  
	
  
•  SD	
  esSmaSon	
  efficiently	
  and	
  accurately	
  provides	
  a	
  
way	
  to	
  analyze	
  T2*	
  precision	
  
•  Useful	
  for	
  prototyping	
  new	
  T2*	
  esSmaSon	
  
methods	
  
•  PotenSally	
  useful	
  for	
  clinical	
  use	
  and	
  re-­‐
determining	
  thresholds	
  for	
  disease	
  management	
  
Conclusions	
  
References	
  
1Department of Electrical Engineering, University of Southern California, Los Angeles, CA 2Laboratory of Cardiac Energetics, National Heart, Lung, and Blood Institute, Bethesda, MD

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Evaluating precision of T2* mapping methods for MRI tissue characterization

  • 1.   [1]  Stark  DD.  Radiology,  1991;  179:333–5.   [2]  Anderson  LJ,  et.  al.  Eur  Heart  J.,  2001;  22(23):2171-­‐9.   [3]  Kellman  PK,  et.  al.  J  Cardiovasc  Magn  Reson.,  2013;  15(1): 56.   The  esSmate  SD’s  turned  out  to  be  significantly  close  to  the  measured   SD’s.  Below  are  the  acquired  phantom  images  and  their  associated  T2*,   measured  SD,  and  esSmated  SD  maps.           Reliable  idenSficaSon  of  T2*   abnormaliSes  is  limited  by     measurement  noise  which     Reduces  the  precision  of  T2*     esSmaSon.  Knowing  how  precise     a  T2*  measurement  is  important     for  prototyping  new  techniques  or     if  a  clinician  is  deciding  how     aggressively  to  treat  a  paSent     with  a  border  line  case.  We     propose  a  method  for     evaluaSng  the  precision  of  T2*     mapping  techniques  by  esSmaSng     the  standard  deviaSon  (SD)  of   each  measurement  thereby   creaSng  an  SD  map.   Evaluating precision of T2* mapping methods for MRI tissue characterization Christopher Sandino1, Hui Xue2, Peter Kellman2     The  standard  deviaSon  for  each  T2*  can  be  esSmated  by  esSmaSng  the   parameters’  (A,  T2*)  covariance  matrix  [3]:                                   All  methods  were  implemented  in  C++  so  that  they  can  be  tested  and  ran   on  an  MRI  scanner  using  medical  image  reconstrucSon  framework   Gadgetron.     Experimental  Valida/on   In  order  to  validate  the  SD  map  formulaSon,     we  made  six  gel  phantoms  made  of  50  mL     soluSons  of  1.5%  agarose  gel,  cupric  sulfate     (CuSO4),  saline  and  varying  concentraSons  of     Feridex  I.V.,  an  intravenous  contrast  agent     containing  colloids  of  iron  oxide.  The     phantoms  were  scanned  N=64  Smes  to  measure  the  “true”  SD  and   compare  them  to  the  esSmated  SD  on  a  per-­‐pixel  basis.     Measured  and  esSmated     SD’s  were  compared  for   two  methods  of  T2*     mapping  (convenSonal   and  truncated)  and     both  showed  >99%     correlaSon.  Note  that     higher  SDs  are  biased     upwards.  These  correspond     to  curves  with  longer  T2*     which  aren’t  sampled  at  long  enough  echo  Smes.                                     In-­‐vivo  studies  showed  relaSvely  high  SD  in  the   bloodpool,  the  heart-­‐lung  interface,  and  areas  where   moSon  occurred.  The  figure  above  shows  a  possible   clinical  applicaSon  where  the  SD  map  is  used  to  mask   unreliable  pixels.                        T2*  mapping  is  a  widely  used,  non-­‐invasive   magneSc  resonance  imaging  (MRI)  technique  used  to   detect  iron  overload  in  Sssue.  High  concentraSons  of   iron  change  the  paramagneSc  properSes  of  the  Sssue   around  it,  including  its  T2*  -­‐  the  transverse  relaxaSon   Sme  aler  an  electromagneSc  excitaSon  [1].                   The  T2*  relaxaSon  curve   is  measured  by  sampling     MR  signal  intensiSes  at     various  echo  Smes  (TE)  [2].   We  know  that  this  curve  is   of  the  form     Yi=A*exp(-­‐TEi/T2*),  so  we     can  use  an  exponenSal     regression  to  esSmate  T2*    for  each  pixel  thereby     creaSng  a  map  such  as  the     one  shown  to  the  right.     This  parScular  paSent  has  low  T2*  in  the  liver,  a  sign   of  iron  overload  due  to  β-­‐thalassemia.   Introduc/on   Objec/ve   Methodology   0 5 10 15 20 0 1 2 3 4 5 6 7 8 Echo Time (ms) SNR Observed Truth Fitted 0 5 10 15 20 0 1 2 3 4 5 6 7 Echo Time (ms) SNR Observed Truth Fitted Low  SNR   Low  T2*   y  =  1.0446x  -­‐  0.0004   R²  =  0.99959   0   1   2   3   0   1   2   3   Es/mated  SD  (ms)   Measured  SD  (ms)   T2* meas. SD meas. SD est. time (ms) RF 1.6 3.9 6.2 8.5 10.8 13.2 15.5 17.8 Mul -echo phantom images Observed Non-Linear Regression Estimated TE y TE y' TE y-y' Residuals Robust Estimation of variance med(abs(residuals)) Compute Covariance Matrix Results   Good  Breath-­‐hold  Poor  Breath-­‐hold   T2*  map   SD  map   T2*  map  w/  mask       •  SD  esSmaSon  efficiently  and  accurately  provides  a   way  to  analyze  T2*  precision   •  Useful  for  prototyping  new  T2*  esSmaSon   methods   •  PotenSally  useful  for  clinical  use  and  re-­‐ determining  thresholds  for  disease  management   Conclusions   References   1Department of Electrical Engineering, University of Southern California, Los Angeles, CA 2Laboratory of Cardiac Energetics, National Heart, Lung, and Blood Institute, Bethesda, MD