Realistic Simulation of Reduced-Dose CT with Noise Modeling and Sinogram Synthesis using DICOM CT Images

학위논문 (박사)-- 서울대학교 대학원 : 협동과정 바이오엔지니어링전공, 2014. 2. 김종효.Reducing the patient dose while maintaining the diagnostic image quality during CT exams is the subject of a growing number of studies, in which simulations of reduced-dose CT with patient data have been used as an effective technique when exploring the potential of various dose reduction techniques. Difficulties in accessing raw sinogram data, however, have restricted the use of this technique to a limited number of institutions. Here, we present a novel reduced-dose CT simulation technique which provides realistic low-dose images without the requirement of raw sinogram data. Two key characteristics of CT systems, the noise equivalent quanta (NEQ) and the algorithmic modulation transfer function (MTF), were measured for various combinations of object attenuation and tube currents by analyzing the noise power spectrum (NPS) of CT images obtained with a set of phantoms. Those measurements were used to develop a comprehensive CT noise model covering the reduced X-ray photon flux, object attenuation, system noise, and bow-tie filter, which was then employed to generate a simulated noise sinogram for the reduced-dose condition with the use of a synthetic sinogram generated from a reference CT image. The simulated noise sinogram was filtered with the algorithmic MTF and back-projected to create a noise CT image, which was then added to the reference CT image, finally providing a simulated reduced-dose CT image. The simulation performance was evaluated in terms of the degree of NPS similarity, the noise magnitude, the bow-tie filter effect, and the streak noise pattern at photon starvation sites with the set of phantom images. In addition, a test trial was applied to patient CT data. The simulation results showed good agreement with actual low-dose CT images in terms of their visual appearance and in a quantitative evaluation test. The magnitude and shape of the NPS curves of the simulated low-dose images agreed well with those of real low-dose images, showing discrepancies of less than +/- 3.2 % in terms of the noise power at the peak height and +/- 1.2 percent in terms of the spatial frequency at the peak height. The magnitudes of the noise measured for 12 different combinations the phantom size, tube current, and reconstruction kernel for the simulated and real low-dose images were very similar, with differences of 0.1 to 4.7%. The p value for a statistical testing of the difference in the noise magnitude ranged from 0.99 to 0.11, showing that there was no difference statistically between the noise magnitudes of the real and simulated low-dose images using our method. The strength and pattern of the streak noise in an anthropomorphic phantom and in the patient case were also consistent with expectations. A novel reduced-dose CT simulation technique was developed which uses only CT images while not requiring raw sinogram data. Our method can provide realistic simulation results under reduced-dose conditions both in terms of the noise magnitude and the textual appearance. This technique has the potential to promote clinical research for patient dose reductions.Contents Abstract i Contents iii List of Figures v List of Tables viii Chapter 1 Introduction 1 Chapter 2 Theory of Noise Modeling 5 2.1 Analysis 7 2.2 Synthesis 11 Chapter 3 Materials and Methods 14 3.1 Overall Procedure 14 3.2 Scanner, scan condition and computing environment 16 3.3 Calculation of NPS and determination of the algorithmic MTF 18 3.4 Measurement of NEQ and system noise 22 3.5 Generation of a synthetic sinogram 23 3.6 Generation of a noise image 26 3.7 Bow-tie filter profile function 29 3.8 Comparison with other methods 30 3.9 Numerical simulation for photon starved condition 33 Chapter 4 Results 34 4.1 Validation of noise model 34 4.1.1 NPS of WGN sinogram and its compensation function 35 4.1.2 NPS comparison with the water phantom 36 4.1.3 Statistical validation of the noise magnitude with the acrylic phantom 39 4.1.4 Effect of the bow-tie filter on noise magnitude profile 42 4.2 Evaluation of noise streak pattern with anthropomorphic phantom 44 4.3 Clinical trial 46 4.4 Performance comparison with numerical simulation 49 Chapter 5 Discussion 59 Chapter 6 Conclusion 56 Reference 60 Abstract(in Korean) 66Docto