Reduced field of view and undersampled PR combined for interventional imaging of a fully dynamic field of view

Abstract

Active catheter imaging was investigated using real-time undersampled projection reconstruction (PR) combined with the temporal filtering technique of reduced field of view (rFOV). Real-time rFOV processing was interactively enabled during highly undersampled catheter imaging, resulting in improved artifact suppression with better temporal resolution than that obtained by view-sharing. Imaging with 64 to 32 projections provided a resolution of 2 x 2 x 8 mm, and four to eight true frames per second. Image artifacts were reduced when rFOV processing was applied to the undersampled images. A comparison with Cartesian rFOV showed that PR image quality is less susceptible to aliasing that results from rFOV imaging with a wholly dynamic outer FOV. Simulations and MRI experiments demonstrated that PR rFOV provides significant artifact suppression, even for a fully dynamic FOV. The near doubling of temporal resolution that is possible with PR rFOV permits accurate monitoring of highly dynamic events, such as catheter movements, and arrhythmias, such as ventricular ectopy.

Copyright 2004 Wiley-Liss, Inc.

Figures

FIG. 1

rFOV PR. a: Angularly interleaved projection sets (Np = 32) collected in alternate time frames for rFOV PR. The gray and black rays indicate separate interleaves. By reducing the number of projections collected by a factor of 2, Δkϕmax is increased and hence the FOV is reduced. After temporal filtering, the low temporal frequencies have the artifact pattern associated with a PSF of 2 · Np projections (b), while high temporal frequencies have artifacts associated with a PSF of Np (c). R is the filter passband width as a fraction of ktmax, the highest temporal frequency.

FIG. 2

A static quality-assurance phantom reconstructed with (a) 64 projections, (b) 32 projections with no rFOV processing, and (c) 32 projections with rFOV processing with a RECT95% filter. Note the dramatic impact of artifact suppression with the use of rFOV.

FIG. 3

Three temporal filters were employed: two low-latency filters (LL80% and LL95%) with 80% and 95% passbands, and one that was a temporal domain representation of an UNFOLD filter with a 95% passband (RECT95%), with a latency of about four frames. The impulse responses are shown.

FIG. 4

Simulation of rFOV PR for a stationary and a moving object. a: Input image and image reconstructed with 64 and 32 Np. Note the characteristic artifact patterns (arrows point to artifacts). b: Stationary object at three consecutive frames after rFOV processing. c: Moving object at three consecutive frames after rFOV processing, for the case of motion with a periodicity of 2 s, and amplitude of motion of 2 pixels. The arrow indicates the direction of translation. d: Summarized results of the simulation for harmonic motion with varying periodicity and amplitude of motion, and the addition of noise in the motion. The general trend shows less artifact signal (normalized by the signal of the artifact before rFOV processing) for smaller amplitudes and longer periods of oscillatory motion. The addition of a noise term to the position results in much less artifact suppression. A periodicity of 1 s corresponds to typical cardiac motion, and a periodicity of 4 s corresponds to typical respiratory motion.

FIG. 5

Comparison of the same 40 Np image (a) raw, and with (b) view-sharing, (c) RECT95% filter, (d) LL80% filter, and (e) LL95% filter. Two signal intensity profiles (labeled I and II), as shown in image a, were obtained crossing the endocardial border, and are plotted in f and g, respectively. The images and profiles demonstrate temporal blur of the endocardial wall, which varies with the temporal filtering applied. They show that rFOV imaging with a low-latency filter provides reduced temporal blur compared toview-sharing, but has slightly increased temporal blur compared to the RECT95% filter. The error bars on the signal intensity profiles are equal in magnitude to the noise measured in the images, and demonstrate that the differences between profiles are due to the temporal filtering applied, and not to image noise.

FIG. 6

Comparison of the same 32 Np catheter image, unfiltered (left) and rFOV filtered (right). The rFOV image has better artifact suppression compared to the raw image.

FIG. 7

PR rFOV image with 48 Np (as compared to Cartesian rFOV with 48 phase-encodings) and matched scan parameters. Scan parameters: SSFP, TR = 4.1 ms, flip angle = 60°, receiver bandwidth = ±62.5 kHz, slice thickness = 8 mm slice. PR: FOV = 32 cm, 160 × 48 Np, Cart: FOV = 34 × 34 cm, 192 × 48 Ny. a: Representative Cartesian frames with least aliasing and most aliasing reconstructed with the LL80% and LL95% filters. b: Representative PR frames with least aliasing and most aliasing reconstructed with the LL80% and LL95% filters. Greater aliasing results from the use of the filter with a greater temporal frequency passband, as expected. The aliasing (shown by arrows) presented here was due to breathing or catheter motions, and appeared in synchrony with the respiratory cycle. In frames in which aliasing was greatest, the aliasing with PR was less obstructive.