Effects of radiation dose reduction in Volume Perfusion CT imaging of acute ischemic stroke

Abstract

Purpose

To examine the influence of radiation dose reduction on image quality and sensitivity of Volume Perfusion CT (VPCT) maps regarding the detection of ischemic brain lesions.

Methods and materials

VPCT data of 20 patients with suspected ischemic stroke acquired at 80 kV and 180 mAs were included. Using realistic reduced-dose simulation, low-dose VPCT datasets with 144 mAs, 108 mAs, 72 mAs and 36 mAs (80 %, 60 %, 40 % and 20 % of the original levels) were generated, resulting in a total of 100 datasets. Perfusion maps were created and signal-to-noise-ratio (SNR) measurements were performed. Qualitative analyses were conducted by two blinded readers, who also assessed the presence/absence of ischemic lesions and scored CBV and CBF maps using a modified ASPECTS-score.

Results

SNR of all low-dose datasets were significantly lower than those of the original datasets (p < .05). All datasets down to 72 mAs (40 %) yielded sufficient image quality and high sensitivity with excellent inter-observer-agreements, whereas 36 mAs datasets (20 %) yielded poor image quality in 15 % of the cases with lower sensitivity and inter-observer-agreements.

Conclusion

Low-dose VPCT using decreased tube currents down to 72 mAs (40 % of original radiation dose) produces sufficient perfusion maps for the detection of ischemic brain lesions.

Key Points

• Perfusion CT is highly accurate for the detection of ischemic brain lesions

• Perfusion CT results in high radiation exposure, therefore low-dose protocols are required

• Reduction of tube current down to 72 mAs produces sufficient perfusion maps

Appendix

Overview of the realistic reduced-dose simulation technique

Realism of added noise pattern in low dose CT simulation depends on two key characteristics of CT systems; the noise equivalent quanta (NEQ) and the algorithmic modulation transfer function (MTF). The NEQ determines the magnitude of noise whereas MTF shapes noise texture. In addition, the presence of highly attenuating objects in the image causes streak noise patterns due to photon starvation phenomenon. Usually, CT raw sinogram data along with a vendor provided noise simulation tool were required to generate simulated low dose CT images, which appropriately reflect those system characteristics. However, it takes great effort and is often unrealizable in many institutions. Our study instead used a novel realistic reduced-dose CT simulation method using only DICOM images based on noise modeling and sinogram synthesis techniques. The overall procedure is illustrated in Fig. S1.

This method uses a comprehensive CT noise model, which employs the NEQ and MTF of the CT system and reflects the reduced x-ray photon flux, object attenuation, system noise, and bow-tie filter. This comprehensive CT noise model enabled generation of appropriate magnitudes and textures of noise without needing to have vendor support.

In addition, this method generates a synthetic sinogram using a DICOM CT image, which is in turn used to produce a simulated noise sinogram for the reduced-dose condition. The magnitude of noise was determined on a pixel-by-pixel basis by taking into account the combined effect of the reduced photon flux, attenuation sum of the object along the ray path, and the system noise arising from the detector circuit. This simulated noise sinogram was then filtered with the algorithmic MTF of the reconstruction kernel and back-projected to create a simulated noise CT image. This simulated noise CT image contain appropriate noise magnitude and texture as well as attenuation-dependent streak patterns. Finally, this noise CT image was then added to the DICOM CT image, finally providing a realistic simulated reduced-dose CT image. This method was validated with a phantom study, which showed that the power spectrum of real and simulated low dose CT were indistinguishable from each other statistically at 12 different combinations of object size, tube current, and reconstruction kernels.