Emily A Aherne1, Ali Serhal1, Ryan J Avery1,2, Alexander Ruh3, Louise M Collins3, Hatice Savas2, Gary Dillehay2, and James C Carr1
1Cardiovascular Imaging, Northwestern University, Chicago, IL, United States, 2Nuclear Imaging, Northwestern University, Chicago, IL, United States, 3Radiology, Northwestern University, Chicago, IL, United States
Synopsis
Cardiac sarcoidosis (CS) is
difficult to clinically diagnose but associated with high morbidity and
mortality. Hybrid PET/MR has been shown to provide clinicians with
complementary data regarding both the pattern and activity of myocardial
disease. We performed a segmental quantitative analysis of MR parameters on 30 patients including T1 and T2 mapping, ECV, scar and strain acquired as part of
a hybrid PET/MR study to evaluate characteristic patterns of imaging in this
heterogeneous disease process. Scar quantification had a significant positive
correlation with T1 and ECV and was negatively correlated with strain.
Introduction
Sarcoidosis is a granulomatous
disease which most commonly affects the lung and hilar lymph nodes but can involve
many different organs. Clinically, cardiac sarcoidosis (CS) is diagnosed only
in a small percentage of these patients but is much more commonly identified at
post mortem. It is associated with significant morbidity and mortality including
arrhythmias, heart failure and sudden death. Advanced cardiac imaging is
recommended in patients with biopsy proven extra-cardiac sarcoidosis who are
symptomatic or have abnormality on electrocardiogram or echocardiogram.1
Hybrid 18F-fluorodeoxyglucose (FDG) positron emission tomography/magnetic
resonance imaging (PET/MRI) has been shown to provide complementary data
regarding both the pattern and activity of myocardial disease in patients
undergoing evaluation for CS, focusing on FDG uptake and late gadolinium
enhancement (LGE) as well as focal measurements of T2 maps in regions of
abnormal FDG uptake. 2,3 By its nature, CS is a patchy disease with
evolution of imaging features between early onset inflammation and late
scarring.4 Strain analysis of echocardiographic and MR images, in
particular global longitudinal strain, has been shown to detect evidence of CS before regional wall- motion
abnormalities become visible or a reduction in left ventricular ejection
fraction is noted.5, 6 We hypothesized that a
segmental quantitative analysis of MR parameters including T1 and T2 mapping,
ECV, scar and strain would allow further elucidation of characteristic patterns
of imaging in this heterogeneous disease process.Methods
45 patients (25 male, age range
27-83 years) with suspected cardiac sarcoidosis referred for PET/MR to evaluate for suspected cardiac sarcoidosis were recruited in an
Institutional Review Board approved study over 20-months. Prior to imaging, all
patients followed strict dietary preparation to suppress physiologic myocardial
FDG uptake. Each patient underwent
simultaneous cardiac MRI including cine imaging, T2 and
pre/post-contrast T1 mapping and LGE as
well as FDG PET cardiac imaging on a hybrid 3T PET/MR system (Biograph-mMR,
Siemens Healthineers).7-9 Blood hematocrit was drawn on the day of
imaging when mapping sequences were performed.
PET images were analyzed in Syngo
(Siemens Healthineers). PET positive cases were defined as those with visually
focal, focal-on-diffuse or diffuse uptake on PET. MR images were anonymized and
analyzed with dedicated software (Circle, cvi42) to determine
segmental quantitative T2, native T1, ECV, scar and strain values (Fig. 1). MRI
positive cases were defined as those with LGE qualitatively suspicious for
sarcoidosis.
Pearson's correlation coefficient (r) was used to investigate
associations between quantity of scar and
T2, native T1, ECV and strain values in each of 16 AHA segments.
Results
2 of 45 studies were excluded due
to non-adherence to dietary preparation. Imaging appearances were variable with
focal, focal on diffuse and diffuse patterns of FDG uptake and patchy areas of
predominantly sub-epicardial and mesocardial areas of fibrosis on late
gadolinium enhanced MRI (Fig.2). Areas of FDG uptake did not always demonstrate
enhancement (Fig.3). 6 were PET+/MR+, 6 PET+/MR-, 9 PET-/MR+ and 20 PET-/MR. 4
studies had no or technically inadequate LGE, 2 of which were PET+ and 2 PET-.
31/43 studies included T1 and T2 mapping sequences. Of the 31 studies with
mapping, 28 also had imaging suitable for scar quantification.
Scar quantification had a significant positive correlation with T1 and ECV
(Fig.4). Some segments of T2 maps had a significant positive correlation with
scar and some had a significant negative correlation (Fig.4). 30/43 patients
had both scar quantification and strain analysis performed. Quantitative scar
was correlated with decreased segmental circumferential and longitudinal
strain, significant in the lateral wall (Fig.5).
Discussion
Cardiac
sarcoidosis is difficult but important to diagnose clinically as it is
associated with high morbidity and mortality. Gold standard myocardial biopsy
is invasive, associated with complications and not always diagnostic given the
patchy nature of the infiltration. Hybrid PET/MR combines the soft tissue
contrast of MRI with metabolic data from PET to offer comprehensive myocardial
evaluation for all stages of infiltration.10 Segmental analysis of
the imaging data acquired showed that scar quantification was positively
correlated with T1 and ECV. In some segments scar was correlated positively
with T2 mapping and in some segments it was negatively correlated. This may
represent the evolution of the granulomatous infiltrate which initially causes
acute myocardial inflammation before gradually causing myocardial fibrosis. As expected, scarring was negatively correlated with
strain.11
Conclusion
A segmental quantitative analysis
of MR parameters including T1 and T2 mapping, ECV, scar and strain acquired as
part of a hybrid PET/MR study may allow further elucidation of characteristic
patterns of imaging in this heterogeneous disease process. Further subgroup analysis
of the segmental data and FDG uptake is required.Acknowledgements
We wish to acknowledge Dr. Michael Markl for advice regarding data analysis methods.References
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