Deep Learning-Based Cardiac MRI Planning from Localizers to Cine Views Using Landmark Detection

Abstract

Rationale and Objectives

This study evaluates a fully automated deep learning framework to enhance the efficiency and accuracy of cardiac MRI planning.

Materials and Methods

In this retrospective study, data from 1023 patients (ages 8–90 years) who underwent cardiac MRI were analyzed, including coronal, sagittal, axial localizers, and short-axis (SAX) and long-axis cine images. Experts manually annotated landmarks, serving as the ground truth for developing deep learning models. The models were assessed using 5-fold cross-validation. Performance metrics included median landmark distances and plane angle differences.

Results

The model achieved robust performance in landmark localization across all cardiac MRI planes. For localizer images, median distances were 5.1 mm (superior) and 7.2 mm (inferior) on coronal views, and 5.6 mm (superior) and 7.5 mm (inferior) on sagittal views. Median distances for axial, 2-chamber, and 4-chamber landmarks were 5.2 mm, 5.2 mm, and 5.6 mm, respectively. In short-axis mid slices, annotations based on the left ventricular center, right ventricular insertion points, and right ventricle obtuse angle had a median error of 5.2 mm, while basal slice valve-based annotations had 4.6 mm error. Angular deviations for SAX planning were 2.0° (2CH) and 1.5° (4CH). For long-axis views, angulation errors were lower using SAX mid slices (3.3° for 2CH, 2.6° for 4CH) compared to SAX base (4.0° and 3.9°, respectively).

Conclusion

A deep learning-based automated workflow for cardiac MRI planning is feasible with improved precision.