Free-breathing 3D cardiac MRI using iterative image-based respiratory motion correction

Abstract

Respiratory motion compensation using diaphragmatic navigator gating with a 5 mm gating window is conventionally used for free-breathing cardiac MRI. Because of the narrow gating window, scan efficiency is low resulting in long scan times, especially for patients with irregular breathing patterns. In this work, a new retrospective motion compensation algorithm is presented to reduce the scan time for free-breathing cardiac MRI that increasing the gating window to 15 mm without compromising image quality. The proposed algorithm iteratively corrects for respiratory-induced cardiac motion by optimizing the sharpness of the heart. To evaluate this technique, two coronary MRI datasets with 1.3 mm(3) resolution were acquired from 11 healthy subjects (seven females, 25 ± 9 years); one using a navigator with a 5 mm gating window acquired in 12.0 ± 2.0 min and one with a 15 mm gating window acquired in 7.1 ± 1.0 min. The images acquired with a 15 mm gating window were corrected using the proposed algorithm and compared to the uncorrected images acquired with the 5 and 15 mm gating windows. The image quality score, sharpness, and length of the three major coronary arteries were equivalent between the corrected images and the images acquired with a 5 mm gating window (P-value > 0.05), while the scan time was reduced by a factor of 1.7.

Keywords: coronary MRI; diaphragmatic navigators; respiratory motion; retrospective motion correction.

Figure 1

Schematic of the proposed motion compensation algorithm. The respiratory pattern measured by a diaphragmatic navigator (NAV) is divided into 15 bins to sort the k-space lines acquired at different states of respiratory cycle. A 3D translation parameter is assigned to each bin to correct the k-space segments acquired at that bin. The sharpness of the image reconstructed from the corrected k-space lines from all bins is measured and passed into an optimization algorithm to update the translation parameters such that the sharpness of the image is maximized. FFT = fast Fourier transform.

Figure 2

Different steps of the proposed motion correction algorithm for the calculation of the translational parameter for each bin. The 3D translation parameter assigned to each bin is updated by an iterative optimization algorithm until the sharpness of the image reconstructed from the motion-corrected k-space lines is maximized. This procedure is stopped when the maximum number of iteration is achieved or the variation of the calculated translation parameters is less than a threshold.

Figure 3

Performance of the proposed algorithm on the phantom undergoing a motion along the SI direction. The motion-corrupted image (b) is corrected using the proposed algorithm by maximizing the image sharpness cost function (c). The corrected image is shown in (d). The corrected image using the NAV information and the reference are shown in (e) and (f), respectively.

Figure 4

Performance of the proposed algorithm on the phantom undergoing a motion along the SI, AP, and RL directions. The amount of motion along these directions is shown in (a). The motion-corrupted image is shown in (b). The sharpness cost function used to estimate the motion parameters and the motion-corrected image are shown in (c-f) and (f), respectively. The corrected image using NAV information and the reference are displayed in (g) and (h), respectively.

Figure 5

Performance of the proposed algorithm on a heart phantom with respiratory motion: (a) the reference image acquired using a diaphragmatic navigator (NAV) with 5 mm gating window; (b) the motion-corrupted image acquired without gating the respiratory motion of the heart phantom; (c) the motion-corrected image using the proposed algorithm; (d) the displacement of the heart phantom due to the respiratory motion through the scan acquisition time; and (e) the histogram of the position of the heart through the scan.

Figure 6

Axial and reformatted images of coronary MRI acquired from a male subject which shows the right coronary artery (RCA), left circumflex (LCX), and left anterior descending (LAD) arteries: the reference image is acquired using a diaphragmatic navigator (NAV) with a 5 mm gating window in ~12 minutes; the motion-corrupted image is acquired using a diaphragmatic NAV with a 15 mm gating window in ~7 minutes; the motion-corrected image is generated by retrospectively correcting the motion-corrupted image using the proposed algorithm.

Figure 7

Axial and reformatted images of coronary MRI showing the right coronary artery (RCA), left circumflex (LCX), and left anterior descending (LAD) systems that are acquired from a female subject: Reference shows the image acquired using a diaphragmatic navigator (NAV) with a 5 mm gating window in ~10 minutes; Motion-corrupted image is acquired using a diaphragmatic NAV with a 15 mm gating window in ~5 minutes. Motion-corrected image demonstrates the performance of the proposed algorithm in the correction of respiratory motion.

Figure 8

Estimated three dimensional translation parameters with respect to the right hemi-diaphragm motion of eleven healthy subjects: (a-c) the estimated translation parameters along the superior-inferior (SI), anterior-posterior (AP), and right-left (RL) directions, respectively.

Figure 9

The mean and standard deviation of the number of occurrence of the NAV in each bin with the size of 1 mm (a) and 4 mm (b).