Quantum Iterative Reconstruction for Low-Dose Ultra-High-Resolution Photon-Counting Detector CT of the Lung

Abstract

The aim of this study was to characterize image quality and to determine the optimal strength levels of a novel iterative reconstruction algorithm (quantum iterative reconstruction, QIR) for low-dose, ultra-high-resolution (UHR) photon-counting detector CT (PCD-CT) of the lung. Images were acquired on a clinical dual-source PCD-CT in the UHR mode and reconstructed with a sharp lung reconstruction kernel at different strength levels of QIR (QIR-1 to QIR-4) and without QIR (QIR-off). Noise power spectrum (NPS) and target transfer function (TTF) were analyzed in a cylindrical phantom. 52 consecutive patients referred for low-dose UHR chest PCD-CT were included (CTDI_vol: 1 ± 0.6 mGy). Quantitative image quality analysis was performed computationally which included the calculation of the global noise index (GNI) and the global signal-to-noise ratio index (GSNRI). The mean attenuation of the lung parenchyma was measured. Two readers graded images qualitatively in terms of overall image quality, image sharpness, and subjective image noise using 5-point Likert scales. In the phantom, an increase in the QIR level slightly decreased spatial resolution and considerably decreased noise amplitude without affecting the frequency content. In patients, GNI decreased from QIR-off (202 ± 34 HU) to QIR-4 (106 ± 18 HU) (p < 0.001) by 48%. GSNRI increased from QIR-off (4.4 ± 0.8) to QIR-4 (8.2 ± 1.6) (p < 0.001) by 87%. Attenuation of lung parenchyma was highly comparable among reconstructions (QIR-off: -849 ± 53 HU to QIR-4: -853 ± 52 HU, p < 0.001). Subjective noise was best in QIR-4 (p < 0.001), while QIR-3 was best for sharpness and overall image quality (p < 0.001). Thus, our phantom and patient study indicates that QIR-3 provides the optimal iterative reconstruction level for low-dose, UHR PCD-CT of the lungs.

Keywords: X-ray computed; imaging; lung; phantoms; tomography.

Figure 1

Visual representation of the quantitative image analysis in patients. First, the lungs were segmented automatically from the CT image sets. Second, mean CT attenuation was measured. Third, noise and signal-to-noise ratio maps were generated. The mode values of the corresponding distributions were defined as GNI and GSNRI.

Figure 2

High contrast TTF as a function of image reconstruction. Graphs show TTF for a high-contrast task (PTFE—Polytetrafluoroethylene) as a function of spatial frequency among reconstructions. A slight shift towards lower frequencies was observed with increasing QIR level indicating slightly decreased spatial resolution.

Figure 3

NPS as a function of image reconstruction. The NPS showed decreasing noise magnitude with increasing QIR level. The shape of the NPS indicated similar noise texture among reconstructions.

Figure 4

Detailed overview of the results from qualitative and quantitative analysis in patients. (A) shows the results from quantitative analysis by means of notched boxplots. A linear decrease of GNI and a linear increase of GSNRI were observed with increasing level of QIR. The CT attenuation was similar among reconstructions. (B) shows the results from qualitative analysis by means of stacked bar plots. While QIR-4 achieved highest ratings for image noise, QIR-3 performed best for sharpness and overall image quality.

Figure 5

Images of a 55-year-old female patient with atypical pneumonia. Noise was reduced considerably when changing from QIR-off to higher levels of QIR. Note the slightly smoother appearance of QIR-4 as opposed to lower levels of QIR indicating slightly reduced sharpness.

Figure 6

Images of a 58-year-old male patient with a solid 6 mm pulmonary nodule in the lateral middle lobe. Noise was reduced considerably when switching from QIR-off to higher levels of QIR. Note the slightly smoothed appearance of the nodule on QIR-4 as opposed to lower levels of QIR.