A computational method is presented in this paper for determining the severity of myocardial infarction of the left ventricle (LV) using its image data. In-silico generated displacement fields for a healthy and damaged LV are used to mimic imaging modalities by adding appropriate levels of noise. To reconstruct the contraction force from the displacement field, a composite material model of the LV is optimized using genetic algorithms and a neural network to return the contraction force and distribution of forces for infarct tissue. The healthy LV contraction force was accurately returned within 1% for all displacement field tests indicating that all imaging methods could be used to measure healthy patient LV displacement fields for the purpose of contraction force reconstruction. With the damaged LV, contraction forces of the healthy region, as well as infarct border and infarct regions were considered. The optimization model found the contraction force distribution within 2% for the healthy region, while for the border zone and infarct regions the average contraction force reconstruction errors were 8.4 kPa and 5.1 kPa, respectively. These errors are reasonably small while no significant SNR dependence was observed. The inverse problem algorithm provided good estimates regardless of the SNR, however, further training of the neural network system is required to improve the robustness of the inversion framework with low contraction forces, since the accuracy of the optimization limited the SNR response.
Sergio C. H. Dempsey and Abbas Samani, "Determining in-silico left ventricular contraction force of myocardial infarct tissue using a composite material model
," Proc. SPIE 10576, Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling, 105761B (Presented at SPIE Medical Imaging: February 15, 2018; Published: 13 March 2018); https://doi.org/10.1117/12.2292914.
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