Yanjie Zhu1,2, Ahmed S. Fahmy2, Chong Duan2, and Reza Nezafat2
1Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, 2Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, United States
Synopsis
Manual
analysis of myocardial tissue mapping is time consuming. Deep learning has a
potential to facilitate the analysis but requires big training datasets. In
this study, a deep fully convolutional neural network, trained using native T1
mapping dataset, is used for T2 and extracellular volume (ECV) quantification
based on transfer learning. We prospectively acquired T2 (401
patients) and ECV maps (381 patients) to access the network performance. Compared
with the manually analyzed reference values, the transfer learning-based
automated analysis platform shows good performance for myocardial T2
and ECV mapping. The platform has potential to fully automate myocardial tissue
mapping.
Introduction
Myocardial
tissue mapping is useful in diagnosis and prognosis of cardiac diseases [1]. However,
T1, T2, and extracellular volume (ECV) are often measured
manually by an experienced observer in analyzing the myocardial tissue mapping
data. This process is time consuming and negatively impacts the reproducibility
and standardization of measurements. Deep learning based technique has a
potential to facilitate analyzing cardiovascular images [2]. We recently
developed a deep learning-based analysis platform for automating native myocardial T1 mapping
analysis. This approach utilizes a deep fully convolutional
neural network (FCN) to automate T1 measurements from T1-weighted
images. Similar network architecture could potentially automate other tissue
parameters, such as ECV or T2 mapping. However, this requires a new
labeled dataset and a dedicated neural network for each individual parameter and
sequence, which is not clinically feasible. In this study, we sought to evaluate
the performance of the pre-trained FCN using native T1 mapping
dataset for automating T2 and ECV measurements analysis, a concept referred
to as transfer learning.Methods
Figure 1 shows the flowchart
of the automated analysis platform. The FCN is used for myocardium
segmentation. It was designed based on U-Net architecture [3] with total of 149
operational layers, including batch normalization, convolutional,
rectified-linear, and dropout layers. The training and testing dataset includes
11550 native T1-weighted images (210 patients) with different
inversion times from native myocardial T1 mapping. The FCN is implemented
using TensorFlow deep learning framework (Google, USA) and trained for 48 hours
on an Intel Core i7-6700K CPU workstation with NVIDIA GeForce GTX Titan 12GB
GPU. In this study, this pre-trained FCN is used for automating T2
and ECV quantification based on transfer learning. Patients with known or
suspected cardiovascular disease referred for a clinical CMR exam were
prospectively recruited. All experiments were approved by the Institutional
Review Board (IRB) and the written informed consent was obtained from each
patient. All imaging experiments were performed using a 1.5T Philips Achieva
system (Philips Healthcare, Best, The Netherlands) with a 32-channel cardiac
coil. We acquired T2 maps in 401 patients (256 male; age: 55±15
years) using slice-interleaved myocardial T2 mapping sequence [4] and
ECV maps in 381 patients (250 male; age: 55±15 years) using slice-interleaved
myocardial T1 mapping sequence [5]. Accuracy of the automatic
measurements was assessed by comparing with the reference values from manual
analysis. Manual analysis was performed by an experienced reader (a
cardiologist with 8-year experience in CMR) using an in-house myocardial tissue
mapping analysis tool (including image registration, curve fitting, and manual
analysis) implemented on Matlab R2009 (The MathWorks, Natick, MA). Pearson
correlation coefficient (R) and
Bland-Altman analysis were used to assess agreement between automated and
manual analyses on per-patient, per-slice, and per-segment analyses.Results
Figure 2 shows representative T
2 and ECV maps reconstructed using the automated
platform and corresponding manually analyzed maps. Automatic T
2
measurement showed a strong correlation with the manual T
2 in
per-patient (390 patients: R = 0.891,
slope = 1.010, P < 0.0001),
per-slice (1873 slices: R = 0.825,
slope = 1.001, P < 0.0001), and
per-segment (9701 segments: R =
0.783, slope = 0.993, P < 0.0001)
analyses (
Figure 3, top row). Automatic and manual T
2 were in good
agreement in per-patient (0.6 ± 5.9 ms, 95% Confidence Interval (CI): -10.9~12.2 ms), per-slice (0.2 ± 9.4
ms, CI: -18.3~18.7 ms), and per-segment T
2 (0.1 ± 13.4 ms, CI:
-26.1~26.2 ms) analyses (
Figure 3, bottom row). Automatic ECV measurements
showed a strong correlation with the manual ECV in per-patient (319 patients: R = 0.918, slope = 0.992, P < 0.0001), per-slice (1489 slices: R = 0.859, slope = 0.989, P < 0.0001), and per-segment (8384
segments: R = 0.806, slope = 0.983, P < 0.0001) analyses (
Figure 4, top
row). The automatic and manual ECV values were in good agreement in per-patient
(-0.2 ± 3.2%, CI: -6.6~6.3%), per-slice (-0.2 ± 4.9%, CI: -9.8~9.4%), and
per-segment ECV (-0.3 ± 7.0%, CI:-14.0~13.4%) analyses.
Table 1 shows the total patient, slice, and image
numbers as well as the corresponding successful rates using the automated
analysis for myocardial T
2 and ECV mapping (
Figure 4, bottom row).
Conclusion
The transfer
learning-based automated analysis platform shows good performance for myocardial
T2 and ECV mapping and has potential to fully automate myocardial
tissue mapping, allowing standardization of data analysis. Further studies are warranted to evaluate the performance
of this technique for myocardial tissue maps acquired at different field
strengths and from different vendors.Acknowledgements
No acknowledgement found.References
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