Quantification of Hepatic Steatosis with MRI Quantification of

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Quantification of Hepatic
Steatosis with MRI
Scott B. Reeder, MD, PhD
International Society for Magnetic Resonance in Medicine
Sociedad Mexicana de Radiologia e Imagen (SMRI)
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Mexico City
June 5, 2014
100%
0%
Department of Radiology
University of Wisconsin
Madison, WI
Disclosure
UW receives support from GE and Bracco
Investigational Pulse Sequences
Learning Objectives
• Understand the importance and prevalence of
Fatty liver disease
• Understand the basic requirements of quantitative
imaging biomarkers
• Be familiar with emerging quantitative biomarkers
of liver fat using MRI
Case: 61yo obese female
• Obese, type II diabetes
• No known liver disease, No EtOH
• Presents with cryptogenic cirrhosis
• Develops HCC 1 year after presentation
• Necessitated liver transplant
Presumed Etiology: Non-Alcoholic Fatty Liver Disease
Non-Alcoholic Fatty Liver Disease
Non-Alcoholic Fatty Liver Disease (NAFLD)
• First described by Ludwig et al (Mayo Clin Proc 1980)
• Most common cause of chronic liver disease
– 30% of people in the USA (100 million) have fatty liver disease
(Harrison et al, ClinLivDis 2004)
– 10% of all children have fatty liver disease
(Schwimmer et al, Semin Liver Dis 2007)
• Fatty liver can progress to injury and scarring, leading to
– Cirrhosis
– Liver failure
– Hepatocellular carcinoma (HCC)
• Fatty Liver Disease: a feature of the “Metabolic Syndrome”
– Obesity, Diabetes (type II)
– Increasing cause of cancer, cardiovascular disease, ? Diabetes type II
– Underlying etiology: Insulin Resistance
Does Fat Matter?
• Elevated risk of cirrhosis, liver failure, HCC
• NAFLD is an independent risk factor for
cardiovascular disease
– Schindhelm et al 2007 Curr Diab Rep
• Steatosis may be causative of DM type II
- Ekstedt et al 2006 Hepatology
• 6-fold increase in malignancy
- Rubenstein et al Hepatology 2008
Yes, Liver Fat Matters …
NAFLD: Pathophysiology
“2nd Hit”
Metabolic Syndrome
Insulin
Resistance
Fatty Liver
(steatosis)
? Iron
? Genetic
? Infection
Fibrosis
(NASH)
Cirrhosis &
Liver Failure
Improving insulin
sensitivity reduces
steatosis
Treatment:
• Exercise
• Caloric restriction
• Drugs
• Surgery
Treatment
Early
Diagnosis
with MRI
Normal
Liver
Intracellular
accumulation
of
triglycerides in hepatocytes
Quantitative biomarkers are needed for …
detection of disease
staging of disease
treatment monitoring
Fatty Liver Disease: Terminology
NAFLD
- Isolated Steatosis
NASH
NASH is aggressive
subset of NAFLD
NASH has worse prognosis,
but isolated steatosis is not
“benign” or “simple”
-Steatosis
-Ballooning degeneration
-Inflammation
-Fibrosis
Key Clinical Issues:
• Identification of NASH
• Risk factors for progression to NASH
Non-Alcoholic Fatty Liver Disease
At the University of Wisconsin, fatty liver disease is …
- 2nd leading cause of liver failure
- 3rd leading indication for liver transplantation
- Expected to be the leading cause of liver failure in a decade
Prevalence of Childhood Obesity in the USA, 1971-2006
Current Diagnosis of Fatty Liver: Biopsy
• Expensive
• Risky (death 1:10000, hospitalization 1:1000)
• Sampling errors
- Liver disease is patchy
- Sampling 1/50,000th of the organ inherently flawed
- Agreement between two adjacent biopsies = 62% (Ratziu et al 2005)
Same Patient
Biopsy 1
Biopsy 2
• Utility of biopsy very limited in children
- Missed or delayed diagnosis
Histology images courtesy Claude Sirlin, MD
Detection of Fat with MRI:
In and Opposed Phase (2-point Dixon)
Opposed-Phase (W-F)
In-Phase (W+F)
Two Point Dixon Imaging
IP = 4.6ms (at 1.5T)
Signal
IP
IP
OP
IP
OP
IP
OP
Echo Time (TE)
OP = 2.3ms (at 1.5T)
IP
OP
Patterns of Steatosis
Patient 2
Diffuse
Diffuse with mass like sparing
Patient 3
In-Phase
Geographic
Patient 4
Patient 1
Opposed-Phase
Lobar
… Others …
Hamer et al Radiographics 2006
Fat Quantification: Why Quantitative?
• Qualitative methods limited
• Quantification needed for thresholds
- Thresholds for early diagnosis
- Different thresholds for different applications
-
eg. screening vs diagnosis
• Treatment monitoring
• Surrogate biomarkers in drugs studies
• Standardization
- across protocols, platforms, sites
- widespread clinically applicability requires standardization
- Standardization requires a quantitative biomarker
Most biomarkers in medicine are quantitative – why not MRI ?
Classes of Fat Quantification Methods
1. With/without fat suppression
- eg. compare T2 without and out fat saturation
2. “Magnitude MRI” (MRI-M)
-
Two or more magnitude images acquired
in/opposed phase
3. “Complex MRI” (MRI-C)
-
Chemical shift based water-fat separation from
complex source images
Imaging Methods for Quantifying NAFLD
add…
Water
Fat
Measured signal
Chemical Shift Based Fat-Water Separation
Water
Fat
100%
Fat Fraction
Fat-Fraction independent
of coil sensitivity
0%
Opposed-Phase
OP = W-F
In-Phase
IP = W+F
IP - OP)
F
(
h=
=
W+F
2 IP
50%
35%
0%
IOP Fat Signal Fraction Map
Quantitative Biomarkers of Steatosis
Confounding Sources of Bias
• Quantitative MRI biomarker for fat requires
consideration of …
–
–
–
–
–
T1 bias
T2* decay
Multiple fat peaks
Noise bias
Eddy Currents
MRI-M
MRI-C
MRI-C has more Potential Sources of Bias, but has Larger
Dynamic Range: 0-100% Fat Fraction
Sources of Bias: Noise
• Using magnitude images to calculate fat-fraction
– Leads to bias from non-zero noise at low fat-fractions
Magnitude
discrimination
(Liu et al, 2007)
η = 1-W/(W+F)
Genetic
? Severe
Hemochromatosis
Steatosis ?
Opposed
In Phase
Phase?
TE=4.8ms
Opposed
In-Phase?
Phase
TE=2.4ms
For IOP imaging, fat and iron have opposite effects!
Simultaneous Estimation: T2*, Water, Fat
•
•
•
•
•
Combined T2* into signal model
Yu et al JMRI 2007 (MRI-C)
Bydder et al MRI 2008 (MRI-M)
O’Regan 2009 Radiology (MRI-C)
Permits simultaneous calculation of water, fat and T2*
Water
Fat
R2*
Yu et al, MRM 2007
Sources of Bias: Multiple Peaks of Fat
• Many metabolites have more than one spectral peak
– Fat has multiple spectral peaks, several near water
– Leads to incomplete separation of water and fat
– Source of “gray” fat on many fat suppression methods
210Hz
Water
-47Hz
23Hz 117Hz
159Hz
236Hz
Sources of Bias: Multiple Peaks of Fat
• “Spectral modeling”
– Must know the relative amplitudes a priori
– Frequencies and relative amplitudes constant
(Hamilton et al)
– No additional information required
– Bydder et al 2008
– Yu et al 2008
T1 Weighted IDEAL-FSE (Yu et al)
Same Window/Level
Water
With spectral modeling
Confounder-Corrected MRI: MRI-C vs MRS
r2=0.91
slope=0.66 ± 0.003, p<10-17
intercept=2.2% ± 0.3%, p<10-14
r2=0.86
slope=0.91 ± 0.05, p=0.08
Intercept=4.6% ± 0.5%, p<10-14
No MP
No T2*
With MP
No T2*
r2=0.76
slope=0.71 ± 0.05, p=10-6
intercept=-0.3% ± 0.5%, p=0.50
No MP
With T2*
r2=0.99
slope=1.00 ± 0.01, p=0.77
Intercept=0.2% ± 0.1%, p=0.19
With MP
With T2*
MRI PDFF vs. Spectroscopy PDFF
35.0
30.0
MRI PDFF
25.0
20.0
15.0
y = 0.9853x + 0.5933
R² = 0.97639
10.0
5.0
0.0
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
Spectroscopy PDFF
Siemens 3.0T widebore
Duke University
Fananapazir et al ISMRM 2013
•
•
•
•
Three sites
7 magnets
1.5T, 3T
Two vendors
Data courtesy Claude Sirlin, MD
Different Methods to Measure
Unconfounded Fat Fraction
Images courtesy Claude Sirlin, MD
Different Methods to Measure
Unconfounded Fat Fraction
Images courtesy Claude Sirlin, MD
Results: MRI vs. Biopsy
20
MRI-C = 0.32 * Biopsy + 1.5
R² = 0.91
MRI (%)
15
10
5
0
0%
-5
15%
30%
45%
Biopsy
60%
75%
Results: MRI vs Biopsy
Biopsy sample
4 regions - Aperio
Quantification
Image pro plus
Segmentation
Image pro plus
Results: Biopsy quantification
20
R2 = 0.99
y = 0.984x + 0.629
R² = 0.986
slope = 0.98 ± 0.03
intercept = 0.63 ± 0.25
MRI (%)
15
10
5
0
0
-5
5
10
15
Biopsy Quantification (%)
20
Differentiate grade 2 vs 3 steatosis with 93% sensitivity, 85% specificity
(cutoff of PDFF of 15%). AUC = 0.97 (no fibrosis), 0.861 (with fibrosis)
n=86
Idilman et al Radiology 2013
AUC
0.989 for 0 vs >=1
0.825 for 0,1 vs 2, 3
0.892 for 0, 1, 2 vs 3
n=77
Tang et al Radiology 2013
PDFF
Study of Health in Pomerania
Jens Kühn, MD
University of Greifswald
40
46
37
42
n=2,814
40
42
40
39
Med. PDFF
= 4% (0-46%)
mild
moderate
high
= 27.7% (n=779)
= 10.6% (n=298)
= 1.7% (n= 49)
44
40
40% Hepatic Steatosis
41 yo M with h/o diabetes and Dyslipidemia:
Triglyceridemia (TG=1022)
100%
31%
51%
35%
55%
10/09/09
(baseline)
0%
12/15/09
After Treatment
Note decrease in fat-fraction and decrease in the size of the liver
Case: 46yo M elevated AST/ALT, 36 lbs
weight gain (4 years), presumed NAFLD
Clinical Question - NASH vs Steatosis?
Opposed-Phase
In-Phase
Case: 51yo F Acute Alcoholic Steatohepatitis
T2W SSFSE
HU = 16
(non-con)
CT
Hepatomegaly
Steatosis
In-phase
Opposed Phase
Case: 31 yo man with family Hx of hemochromatosis,
elevated ferritin. MRI ordered to r/o iron overload
Opposed Phase
In Phase
Conventional IOP Imaging
Signal dropout on
opposed phase imaging
consistent with steatosis only
250 s-1
100%
Complex MRI
Severe steatosis: PDFF = 28%
(normal < 5-6%)
28%
90s-1
0 s-1
0%
Fat-Fraction Map
Mild iron overload: R2*=90s-1
(normal < 50-60s-1)
R2* Map
Diagnosis: NAFLD and hemochromatosis
(Iron overload missed on IOP imaging)
Case: 31 yo man with family Hx of hemochromatosis,
elevated ferritin. MRI ordered to r/o iron overload
H&E (4x)
Perl’s Blue (10x)
Case: 10 yo boy with abdominal pain
T2 with Fat-Sat
Out of Phase
Coronal T2 SSFSE
In Phase
Case: 10 yo boy with abdominal pain
10kPa
100%
200 s-1
22%
0%
40ms
0 s-1
Biopsy: severe steatohepatitis with severe fibrosis,
just short of cirrhosis
Case: 10 yo male with abdominal pain
Grade 2 steatosis, Grade 3 steatohepatitis, stage 3-4 fibrosis
Findings and clinical history consistent with NASH
Case: 16 yo girl with PCOS
3.6kPa
100%
200s-1
31%
0%
20ms
0s-1
Biopsy: severe steatohepatitis with mild fibrosis
Study: Adolescent Girls
3T
35%
Mean Fat Fraction
30%
r2 = 0.98
Slope: 1.03 (p<0.001)
Intercept: 0.73 (p=0.01)
25%
20%
15%
10%
5%
0%
0%
5%
10%
15%
20%
25%
30%
35%
MRS Spectroscopy Fat-Fraction (%)
Rehm et al ISMRM 2011
Study: Adolescent Girls
100%
3%
32%
17%
0%
kg/m2
BMI =35
ALT = 19 U/L
Insulin = 25 μU/mL
kg/m2
BMI =31
ALT = 48 U/L
Insulin = 46 μU/mL
kg/m2
BMI =30
ALT = 29 U/L
Insulin = 71 μU/mL
Good correlation with ALT
Moderate correlation with insulin
Poor correlation of liver fat with BMI
Rehm et al ISMRM 2011
Learning Objectives
• Understand the importance and prevalence of
Fatty liver disease
• Understand the basic requirements of quantitative
imaging biomarkers
• Be familiar with emerging quantitative biomarkers
of liver fat using MRI
Thank you!
•
•
•
•
•
•
•
•
•
•
•
•
Rashmi Agni, MD
Jean Brittain, PhD
Diego Hernando, PhD
Cathy Hines, PhD
Jen Kuhn, MD
Ben Landgraf
Alejandro Roldan, PhD
Claude Sirlin, MD
Oliver Wieben, MD
Emily Winslow, MD
Huanzhou Yu, PhD
Jennifer Rehm, MD
Grant Support
• WARF Accelerator
• NIH: RO1 DK083380
RO1 DK088925
RC1 EB010384
RO1 DK096169

Quantification of Hepatic Steatosis with MRI Quantification of

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