Image reconstruction in higher dimensions: myocardial perfusion imaging of tracer dynamics with cardiac motion due to deformation and respiration

Abstract

Myocardial perfusion imaging (MPI) using slow rotating large field of view cameras requires spatiotemporal reconstruction of dynamically acquired data to capture the time variation of the radiotracer concentration. In vivo, MPI contains additional degrees of freedom involving unavoidable motion of the heart due to quasiperiodic beating and the effects of respiration, which can severely degrade the quality of the images. This work develops a technique for a single photon emission computed tomography (SPECT) that reconstructs the distribution of the radiotracer concentration in the myocardium using a tensor product of different sets of basis functions that approximately describe the spatiotemporal variation of the radiotracer concentration and the motion of the heart. In this study the temporal B-spline basis functions are chosen to reflect the dynamics of the radiotracer, while the intrinsic deformation and the extrinsic motion of the heart are described by a product of a discrete set of Gaussian basis functions. Reconstruction results are presented showing the dynamics of the tracer in the myocardium as it deforms due to cardiac beating, and is displaced due to respiratory motion. These results are compared with the conventional 4D-spatiotemporal reconstruction method that models only the temporal changes of the tracer activity. The higher dimensional reconstruction method proposed here improves bias, yet the signal-to-noise ratio (SNR) decreases due to redistribution of the counts over the cardiac-respiratory gates. However, there is a trade-off between the number of gates and the number of projections per gate to achieve high contrast images.

Handling Big Data in medical imaging: Iterative reconstruction with large-scale automated parallel computation

“…(Second Row) The coronal view of the reconstruction of dynamic data 30 sec after the injection of 99m Tc-tetrofosmin. The reconstruction of the gated cardiac data was performed using a 5D spatiotemporal reconstruction algorithm described in [7]. In this simulation, 6 B-spline basis functions were used for the temporal changes in the tracer concentration and 8 Gaussian basis functions were used for the cardiac deformation due to the heart beating.…”

Comparison of sparse domain approaches for 4D SPECT dynamic image reconstruction

“…Algorithms have advanced, including modeling of cardiac motion in dynamic 5D reconstruction, modeling of cardiac and respiratory motion in dynamic 6D reconstruction, and directly fitting compartment models . Our SIFADS method is a 4D reconstruction approach that directly estimates time activity curves (TACs) from projections of dynamic data acquired from slowly rotating gamma cameras and differs from these previous methods where first dynamic image frames are reconstructed, and then, from which TACs can be generated.…”

“…36 Our work does not address cardiac or any motion at this stage. 28,29,37 The paper is organized as follows. In Section 2, we present the algorithm and some measures that we use in the current work to evaluate the algorithm's performance.…”

SPECT quantification of myocardial blood flow: A journey of a thousand miles begins with a single step (Lao Tzu, Chinese philosopher, 604-531 BC)

“…6 Their study takes advantage of a unique '4D reconstruction' method for dynamic SPECT imaging, specially developed inhouse for cardiac perfusion imaging. 4 This type of advanced parametric image reconstruction represents the measured data as a combination of pre-defined timeactivity curves (also called shape or basis functions) for every voxel in the field of view. While there is not yet an accepted 'standard basis' for 4D image reconstruction, the authors have shown promising results using their chosen shape functions in several earlier simulation and human studies.…”

“…While there is not yet an accepted 'standard basis' for 4D image reconstruction, the authors have shown promising results using their chosen shape functions in several earlier simulation and human studies. [3][4][5] If these shape functions can provide a good fit to the measured dynamic data, then the modeled tracer distribution or 'parametric images' can be visualized post hoc at any time point from the start to end of the scan. This has the potential to allow visualization of tracer uptake in very short time intervals (i.e., 5 seconds in the present study) but with the image quality and count statistics derived from the much longer dynamic scan.…”