Teodora Chitiboi1, Bogdan Georgescu1, Jens Wetzl2, Indraneel Borgohain1, Christian Geppert2, Stefan K Piechnik3, Stefan Neubauer3, Steffen Petersen4, and Puneet Sharma1
1Siemens Healthineers, Princeton, NJ, United States, 2Magnetic Resonance, Siemens Healthcare, Erlangen, Germany, 3Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom, 4NIHR Biomedical Research Centre at Barts, Queen Mary University of London, London, United Kingdom
Synopsis
Deep learning enables fully
automatic strain analysis from CINE MRI on large subject cohorts. Deep learning
neural nets were trained to segment the heart chambers from CINE MRI using
manually annotated ground truth. After validation on more than 1700 different
patient datasets, the models were used to generate segmentations as the first
step of a fully automatic strain analysis pipeline for 460 subjects. We found
significant differences associated with gender (strain magnitude smaller for
males), height (lower strain magnitude for patients taller than 170 cm) and age
(lower circumferential and longitudinal strain for subjects older than 60 years).
Introduction
MRI enables accurate quantification of cardiac chamber volumes and
function. Fully automatic cardiac function assessment from CINE MRI has yet to
become a standard in clinical practice, but deep learning approaches show increasing
potential when leveraging great amounts of annotated data. We trained deep neural networks to perform joint left and
right ventricle segmentation from short axis images and independently left and
right atria segmentation from long axis images and validate its performance on
large set of more than 1700 normal subjects. We then fully automatically quantified
strain parameters for a subset of patients to characterize myocardial
deformation, analyzing differences associated with age, gender, and height. This research has been conducted using the UK Biobank
Resource1 (access application 2964).Methods
Short-axis (SAX) and long-axis (LAX) CMR CINE bSSFP available from the
UK Biobank Resource 1 had been
acquired using a standard protocol 2 (8 mm slice thickness, spatial
resolution between 1.8-2.1 x 1.8-2.1 mm, and 31 ms temporal resolution) at 1.5 T (MAGNETOM Aera,
Siemens Healthcare, Erlangen, Germany). Manually annotated ground truth
for the end-systolic (ES)
and end-diastolic (ED) phases by an expert observer was available from the UK Biobank Resource 1.
The deep
neural networks for image segmentation were designed based on the U-net
architecture 3 using 5 densely connected blocks 4 in the encoding and the decoding
part of the network, respectively. For the SAX stack, a 4-class model was
trained to jointly segment the left-ventricular (LV) blood pool, myocardium and
the right ventricle (RV), with an additional class for the background. Data
from 3000 subjects was used for training (10% reserved for validation) and 1719
unseen cases for testing, which amounts to 22640 individual SAX images. In LAX,
a four-chamber view was considered for segmenting the atria. Two binary models
were independently trained to segment the left (LA) and right atria (RA) and
the background. 2809 cases were used for training of the LA and 2756 for the
right atrium RA, while 2000 cases were reserved for testing. The results on the
testing data were compared with the ground truth segmentation.
Smooth contours were
automatically extracted from the probabilistic results of the neural nets and
propagated to all image frames. A dense temporal deformation field was computed
by inverse-consistent deformable registration 5. Based
on this, the time-varying Lagrangian strain tensor was computed for the LV for
a random subset of 460 healthy subjects from the testing set 6.
The strain tensors were projected in cylindrical coordinates centered with
respect to the LV axes, and global radial, circumferential and longitudinal strain
magnitude curves (GRS, GCS, and GLS) were computed for each patient. The
average strain curves were analyzed for patient subsets divided according to
age, gender, height and body-mass index (BMI). Subjects with missing
information were excluded for the subgroup analysis. The maximum strain
magnitude was computed at ES and a middle diastolic frame was considered half-way
between the ES frame and the end of the series to assess diastolic function.
Statistical significance was determined using the T-test.Results
Table 1
shows the dice scores obtained after deep-learning segmentation on the testing
set for each segmented cardiac structure in short and long axis (LV 96.2%,
myocardium 89.4%, RV 92.9%, LA 92.8%, RA 94.7%). Figure 1 shows the segmented
contours for the LV, RV and myocardium in short and long axis, and strain
curves for an example test dataset. Figure 2 shows the average GRS, GCS, GLS
magnitudes over time (±SD) for the entire patient population, as well as for subgroups separated
by age and gender. Table 2 shows the significant differences between patient
subgroups divided by age, gender, and height. No significant differences were
found in connection to BMI.Discussion
The
performance of the machine-learning segmentation algorithm is similar or
slightly better compared to the state-of-the-art 7.
The reported strain values fall under standard reported values for normal subjects 8
and are in agreement with similar findings regarding age and gender differences 9,10 . While there was a significant difference in strain magnitude by gender, we
also found a small but significant difference in strain magnitude associated to
overall height or body size, which could party account for the gender
differences. Radial strain magnitude was significantly larger for females,
younger and shorter populations, both and ES and mid-diastole. While the
end-diastolic circumferential and longitudinal strains were similar for the
younger and older age groups, the mid-diastolic strain was significantly lower
for subjects older than 60. This suggests that this older population may show
signs of diastolic disfunction. While no significant differences were found in
connection to BMI, we mention that the study population was generally healthy
and very few patients were underweight or obese.Conclusion
In
conclusion, we showed that we were able to reliably perform fully automatic
ventricular volume quantification from short-axis CINE MRI, followed by
left-ventricular strain quantification. Deep-learning powered algorithms enable
the automatic quantification of large subject cohorts, which can consolidate
insights in cardiac anatomy and physiology and potentially reduce the time
routinely dedicated to data processing.
Acknowledgements
The concepts and information presented in this paper are based on research results that are not commercially available.References
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