Improved accuracy of quantitative parameter estimates in dynamic contrast-enhanced CT study with low temporal resolution

Authors

  • Kim Sun Mo,

    1. Radiation Medicine Program, Princess Margaret Hospital/University Health Network, Toronto, Ontario M5G 2M9, Canada
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  • Haider Masoom A.,

    1. Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, Ontario M4N 3M5, Canada and Department of Medical Imaging, University of Toronto, Toronto, Ontario M5G 2M9, Canada
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  • Jaffray David A.,

    1. Radiation Medicine Program, Princess Margaret Hospital/University Health Network, Toronto, Ontario M5G 2M9, Canada and Department of Radiation Oncology, University of Toronto, Toronto, Ontario M5G 2M9, Canada
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  • Yeung Ivan W. T.

    1. Radiation Medicine Program, Princess Margaret Hospital/University Health Network, Toronto, Ontario M5G 2M9, Canada; Department of Medical Physics, Stronach Regional Cancer Centre, Southlake Regional Health Centre, Newmarket, Ontario L3Y 2P9, Canada; and Department of Radiation Oncology, University of Toronto, Toronto, Ontario M5G 2M9, Canada
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Abstract

Purpose:

A previously proposed method to reduce radiation dose to patient in dynamic contrast-enhanced (DCE) CT is enhanced by principal component analysis (PCA) filtering which improves the signal-to-noise ratio (SNR) of time-concentration curves in the DCE-CT study. The efficacy of the combined method to maintain the accuracy of kinetic parameter estimates at low temporal resolution is investigated with pixel-by-pixel kinetic analysis of DCE-CT data.

Methods:

The method is based on DCE-CT scanning performed with low temporal resolution to reduce the radiation dose to the patient. The arterial input function (AIF) with high temporal resolution can be generated with a coarsely sampled AIF through a previously published method of AIF estimation. To increase the SNR of time-concentration curves (tissue curves), first, a region-of-interest is segmented into squares composed of 3 × 3 pixels in size. Subsequently, the PCA filtering combined with a fraction of residual information criterion is applied to all the segmented squares for further improvement of their SNRs. The proposed method was applied to each DCE-CT data set of a cohort of 14 patients at varying levels of down-sampling. The kinetic analyses using the modified Tofts' model and singular value decomposition method, then, were carried out for each of the down-sampling schemes between the intervals from 2 to 15 s. The results were compared with analyses done with the measured data in high temporal resolution (i.e., original scanning frequency) as the reference.

Results:

The patients' AIFs were estimated to high accuracy based on the 11 orthonormal bases of arterial impulse responses established in the previous paper. In addition, noise in the images was effectively reduced by using five principal components of the tissue curves for filtering. Kinetic analyses using the proposed method showed superior results compared to those with down-sampling alone; they were able to maintain the accuracy in the quantitative histogram parameters of volume transfer constant [standard deviation (SD), 98th percentile, and range], rate constant (SD), blood volume fraction (mean, SD, 98th percentile, and range), and blood flow (mean, SD, median, 98th percentile, and range) for sampling intervals between 10 and 15 s.

Conclusions:

The proposed method of PCA filtering combined with the AIF estimation technique allows low frequency scanning for DCE-CT study to reduce patient radiation dose. The results indicate that the method is useful in pixel-by-pixel kinetic analysis of DCE-CT data for patients with cervical cancer.

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