Noise spatial nonuniformity and the impact of statistical image reconstruction in CT myocardial perfusion imaging

Abstract

Purpose: To achieve high temporal resolution in CT myocardial perfusion imaging (MPI), images are often reconstructed using filtered backprojection (FBP) algorithms from data acquired within a short-scan angular range. However, the variation in the central angle from one time frame to the next in gated short scans has been shown to create detrimental partial scan artifacts when performing quantitative MPI measurements. This study has two main purposes. (1) To demonstrate the existence of a distinct detrimental effect in short-scan FBP, i.e., the introduction of a nonuniform spatial image noise distribution; this nonuniformity can lead to unexpectedly high image noise and streaking artifacts, which may affect CT MPI quantification. (2) To demonstrate that statistical image reconstruction (SIR) algorithms can be a potential solution to address the nonuniform spatial noise distribution problem and can also lead to radiation dose reduction in the context of CT MPI.

Methods: Projection datasets from a numerically simulated perfusion phantom and an in vivo animal myocardial perfusion CT scan were used in this study. In the numerical phantom, multiple realizations of Poisson noise were added to projection data at each time frame to investigate the spatial distribution of noise. Images from all datasets were reconstructed using both FBP and SIR reconstruction algorithms. To quantify the spatial distribution of noise, the mean and standard deviation were measured in several regions of interest (ROIs) and analyzed across time frames. In the in vivo study, two low-dose scans at tube currents of 25 and 50 mA were reconstructed using FBP and SIR. Quantitative perfusion metrics, namely, the normalized upslope (NUS), myocardial blood volume (MBV), and first moment transit time (FMT), were measured for two ROIs and compared to reference values obtained from a high-dose scan performed at 500 mA.

Results: Images reconstructed using FBP showed a highly nonuniform spatial distribution of noise. This spatial nonuniformity led to large fluctuations in the temporal direction. In the numerical phantom study, the level of noise was shown to vary by as much as 87% within a given image, and as much as 110% between different time frames for a ROI far from isocenter. The spatially nonuniform noise pattern was shown to correlate with the source trajectory and the object structure. In contrast, images reconstructed using SIR showed a highly uniform spatial distribution of noise, leading to smaller unexpected noise fluctuations in the temporal direction when a short scan angular range was used. In the numerical phantom study, the noise varied by less than 37% within a given image, and by less than 20% between different time frames. Also, the noise standard deviation in SIR images was on average half of that of FBP images. In the in vivo studies, the deviation observed between quantitative perfusion metrics measured from low-dose scans and high-dose scans was mitigated when SIR was used instead of FBP to reconstruct images.

Conclusions: (1) Images reconstructed using FBP suffered from nonuniform spatial noise levels. This nonuniformity is another manifestation of the detrimental effects caused by short-scan reconstruction in CT MPI. (2) Images reconstructed using SIR had a much lower and more uniform noise level and thus can be used as a potential solution to address the FBP nonuniformity. (3) Given the improvement in the accuracy of the perfusion metrics when using SIR, it may be desirable to use a statistical reconstruction framework to perform low-dose dynamic CT MPI.

Figure 1

Reconstructions of the numerical phantom at different time frames. (a)–(d) Phantoms were reconstructed using FBP. (e)–(h)Maps of the standard deviation in the FBP reconstruction. (i)–(l) Phantoms were reconstructed using SIR. (m)–(p) The standard deviation maps calculated from SIR reconstructions. The noise is generally more uniform and of lower level in SIR images than in FBP reconstructions. The noise in SIR images has a negligible dependence on the source trajectory. The line segments superposed on the FBP reconstructions represent the short-scan source trajectory. The ROIs in (a) were used for the measurement of enhancement curves (Fig. 2). The display range was set to [0, 0.03] mm⁻¹ for the reconstructions.

Figure 2

Dynamic contrast enhancement curves in the noncardiac muscle ROI [A in Fig. 1a]. Also plotted is the relative noise standard deviation as a function of time in the same region. Notice that the level of noise-induced fluctuations in the enhacement curved match the trend observed in the relative noise standard deviation plots. SIR offers less temporal variations in noise and a lower level than FBP. The noise level simulated an incident fluence of 10⁶ photons/detector element.

Figure 3

Dynamic contrast enhancement curves in the myocardium ROI [B in Fig. 1a]. Also plotted is the relative noise standard deviation as a function of time in the same region. Notice that the level of noise-induced fluctuations in the enhacement curved match the trend observed in the relative noise standard deviation plots. SIR offers less temporal variations in noise and a lower level than FBP. The noise level simulated an incident fluence of 10⁶ photons/detector element.

Figure 4

Dynamic contrast enhancement curves in the left ventricle ROI [C in Fig. 1a]. Also plotted is the relative noise standard deviation as a function of time in the same region. Notice that the level of noise-induced fluctuations in the enhacement curved match the trend observed in the relative noise standard deviation plots. SIR offers less temporal variations in noise and a lower level than FBP. The noise level simulated an incident fluence of 10⁶ photons/detector element.

Figure 5

Dynamic contrast enhancement curves in the infarct ROI [D in Fig. 1a]. Also plotted is the relative noise standard deviation as a function of time in the same region. Notice that the level of noise-induced fluctuations in the enhacement curved match the trend observed in the relative noise standard deviation plots. SIR offers less temporal variations in noise and a lower level than FBP. The noise level simulated an incident fluence of 10⁶ photons/detector element.

Figure 6

Reconstructions of the in vivo porcine datasets at different tube currents. The images were reconstructed using FBP and SIR. The display range was [−1000, 900] HU.

Figure 7

Definition of the regions of interest (ROI) used for the perfusion metric measurements. PM was located near the papillary muscle, while AW was situated in the anterior wall of the myocardium. Both ROIs were 3 × 5 voxels. The display range for this image was [−1000, 900] HU.

Figure 8

Time attenuation curves (TAC) measured from reconstructions of the in vivo porcine datasets at different tube currents (a, b) and the standard deviation at different time frames (c, d). Images were reconstructed using FBP and SIR. The ROI were the measurements were performed was located in the papillary muscle, PM in Fig. 7. Note that in order to optimize the visualization of the data, the range of attenuation coefficient shown is not constant between the different plots. Also, some divergence from the 500 mA curves might be explained by the fact that a different scan was acquired for each tube current setting.

Figure 9

Time attenuation curves (TAC) measured from reconstructions of the in vivo porcine datasets at different tube currents. The images were reconstructed using FBP and SIR. Two ROIs were used for the measurements; their location is shown above. Note that in order to optimize the visualization of the data, the range of attenuation coefficient shown is not constant between the different plots. Also, some divergence from the 500 mA curves might be explained by the fact that a different scan was acquired for each tube current setting.