Use of MRI to assess the prediction of heart motion with gross body motion in myocardial perfusion imaging by stereotracking of markers on the body surface
Authors: King Michael A. , Dey Joyoni , Johnson Karen , Dasari Paul , Mukherjee Joyeeta M. , McNamara Joseph E. , Konik Arda , Lindsay Cliff , Zheng Shaokuan , Coughlin Dennis 
Published: October 2013

Purpose:

The aim of this study is to determine using MRI in volunteers whether the rigid‐body‐motion (RBM) model can be approximately used to estimate the gross body‐motion of the heart from that of external markers on patientˈs chest. Our target clinical application is to use a visual‐tracking‐system (VTS) which employs stereoimaging to estimate heart motion during SPECT/CT and PET/CT myocardial perfusion imaging.

Methods:

To investigate body‐motion separate from the respiration the authors had the volunteers hold their breath during the acquisition of a sequence of two sets of EKG‐triggered MRI sagittal slices. The first set was acquired pre‐motion, and the second postmotion. The motion of the heart within each breath‐hold set of slices was estimated by registration to the semiautomatic 3D segmentation of the heart region in a baseline set acquired using the Navigator technique. The motion of the heart between the pre‐ and postmotion sets was then determined as the difference in the individual motions in comparison to the Navigator sets. An analysis of the combined motion of the individual markers on the chest was used to obtain an estimate of the six‐degree‐of‐freedom RBM from the VTS system. The metric for judging agreement between the motion estimated by MRI and the VTS was the average error. This was defined as the average of the magnitudes of the differences in the vector displacements of all voxels in the heart region. Studies with the Data Spectrum Anthropomorphic Phantom and “No‐Motion” studies in which the volunteer did not intentionally move were used to establish a baseline for agreement. With volunteer studies a t‐test was employed to determine when statistically significant differences in Average Errors occurred compared to the No‐motion studies.

Results:

For phantom acquisitions, the Average Error when the motion was just translation was 0.1 mm. With complex motions, which included a combination of rotations and translations, the Average Error increased to 3.6 mm. In the volunteers the Average Error averaged over all No‐Motion acquisitions was 1.0 mm. For the case of translational motion, which might be expected to be RBM, the Average Error averaged over all volunteer studies increased to 2.6 mm, which was statistically different from the No‐Motion studies. For the case of bends and twists of the torso, which would be expected to challenge the RBM model, the Average Error averaged over all such volunteer studies was 4.9 mm and was again statistically different. Investigations of motion of the arm including just bending at the elbow and leg motion resulted in Average Errors which were not statistically different from the No‐Motion studies. However, when shoulder movement was included with arm motion the Average Error was near that of torso bends and twists, and statistically different.

Conclusions:

Use of the RBM model with VTS predictions of heart motion during reconstruction should decrease the extent of artifacts for the types of patient motion studied. The impact of correction would be less for torso bends and twists, and arm motion which includes the shoulders.