Multi-Frame Image Registration for Automated Ventricular Function Assessment in Single Breath-Hold Cine MRI Using Limited Labels.
Authors
Affiliations (6)
Affiliations (6)
- Medical Image and Data Analysis (MIDAS.lab), Department of Interventional and Diagnostic Radiology, University Hospital of Tuebingen, Tuebingen, Germany.
- Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany.
- School of Computation, Information and Technology, Technical University of Munich (TUM), Munich, Germany.
- TUM University Hospital, Technical University of Munich (TUM), Munich, Germany.
- Department of Computing, Imperial College London, London, UK.
- Department of Radiology, Stanford University, Stanford, California, USA.
Abstract
This study aims to develop an automated framework for operator-independent assessment of cardiac ventricular function from highly accelerated images. We introduce a deep learning framework that generates reliable ventricular volumetric parameters and strain measures from fully sampled and retrospectively accelerated MR images. This method integrates image registration, motion-compensated reconstruction, and segmentation in a synergetic loop for mutual refinement. The evaluation was performed on an in-house dataset of healthy and cardiovascular-diseased subjects. We examined the performance of the underlying tasks, including registration and segmentation, and their impact on derived parameters related to ventricular function. The proposed approach demonstrates robustness to undersampling artifacts and requires limited annotation, while still reducing variability and errors for segmentation and registration. This translates to a <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics><mrow><mn>9</mn> <mo>%</mo></mrow> <annotation>$$ 9\% $$</annotation></semantics> </math> to <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics><mrow><mn>22</mn> <mo>%</mo></mrow> <annotation>$$ 22\% $$</annotation></semantics> </math> increase in Dice similarity compared to existing deep learning methods for left endocardium, left epicardium, and right ventricular delineation. Analysis of the predicted left and right ventricular ejection fraction reveals a strong correlation ( <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics><mrow><mo>></mo> <mn>0</mn> <mo>.</mo> <mn>9</mn></mrow> <annotation>$$ >0.9 $$</annotation></semantics> </math> ) with manual measurements. Moreover, the estimated motion and segmentation masks enable consistent radial and circumferential strain measurements across accelerations up to <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics><mrow><mi>R</mi> <mo>=</mo> <mn>24</mn></mrow> <annotation>$$ R=24 $$</annotation></semantics> </math> . A comprehensive ventricular function analysis can be performed using highly accelerated cine MR data with minimal annotation effort. This multitasking strategy has the potential to enable more accessible cardiac function analysis within a single breath-hold.