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 spatialimagenoise distribution; this nonuniformity can lead to unexpectedly high imagenoise 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 spatialnoise 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 anin 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:
Imagesreconstructed 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, imagesreconstructed 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 thein 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 reconstructimages.Conclusions:
(1) Imagesreconstructed using FBP suffered from nonuniform spatialnoise levels. This nonuniformity is another manifestation of the detrimental effects caused by short-scan reconstruction in CT MPI. (2) Imagesreconstructed 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.
The authors wish to acknowledge partial funding support from the National Institutes of Health (NIH) through R01HL090776 (G.H.C. and M.A.S.) and a NSERC-CRSNG doctoral scholarship (P.T.L.). The authors would also like to thank Dr. Michael S. Van Lysel for his assistance with the animal study. The authors are also grateful for stimulating discussions with Mr. Timothy Szczykutowicz and Mrs. Courtney Jarman, as well as for the editorial assistance from Mr. Stephen Brunner. The authors thank Dr. Brian Nett from GE Healthcare for his help with the processing of raw scanner projection data with noise estimation. Finally, the constructive comments and suggestions from anonymous reviewers are acknowledged.
II. FILTERED BACKPROJECTION
III. STATISTICAL IMAGE RECONSTRUCTION
IV. METHODS AND MATERIALS
IV.A. Numerically simulated dataset
IV.B. In vivo porcine dataset
IV.C. Reconstruction algorithms implementation
IV.D. Quantitative perfusion metrics
V.A. Numerically simulated dataset
V.B. In vivo porcine dataset
VI. DISCUSSION AND CONCLUSIONS
VI.A. Limitations and future work
- Medical image noise
- Medical image reconstruction
- Medical imaging
- Image reconstruction
- Computed tomography
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