Voxelwise characterization of noise for a clinical photon-counting CT scanner with a model-based iterative reconstruction algorithm

Abstract

Background: Photon-counting detector (PCD) technology has the potential to reduce noise in computed tomography (CT). This study aimed to carry out a voxelwise noise characterization for a clinical PCD-CT scanner with a model-based iterative reconstruction algorithm (QIR).

Methods: Forty repeated axial acquisitions (tube voltage 120 kV, tube load 200 mAs, slice thickness 0.4 mm) of a homogeneous water phantom and CTP404 module (Catphan-504) were performed. Water phantom acquisitions were also performed on a conventional energy-integrating detector (EID) scanner with a sinogram/image-based iterative reconstruction algorithm, using similar acquisition/reconstruction parameters. For smooth/sharp kernels, filtered back projection (FBP)- and iterative-reconstructed images were obtained. Noise maps, non-uniformity index (NUI) of noise maps, image noise histograms, and noise power spectrum (NPS) curves were computed.

Results: For FBP-reconstructed images of water phantom, mean noise was (smooth/sharp kernel) 11.7 HU/51.1 HU and 18.3 HU/80.1 HU for PCD-scanner and EID-scanner, respectively, with NUI values for PCD-scanner less than half those for EID-scanner. Percentage noise reduction increased with increasing iterative power, up to (smooth/sharp kernel) 57.7%/72.5% and 56.3%/70.1% for PCD-scanner and EID-scanner, respectively. For PCD-scanner, FBP- and QIR-reconstructed images featured an almost Gaussian distribution of noise values, whose shape did not appreciably vary with iterative power. Noise maps of CTP404 module showed increased NUI values with increasing iterative power, up to (smooth/sharp kernel) 15.7%/9.2%. QIR-reconstructed images showed limited low-frequency shift of NPS peak frequency.

Conclusion: PCD-CT allowed appreciably reducing image noise while improving its spatial uniformity. QIR algorithm decreases image noise without modifying its histogram distribution shape, and partly preserving noise texture.

Relevance statement: This phantom study corroborates the capability of photon-counting detector technology in appreciably reducing CT imaging noise and improving spatial uniformity of noise values, yielding a potential reduction of radiation exposure, though this needs to be assessed in more detail.

Key points: First voxelwise characterization of noise for a clinical CT scanner with photon-counting detector technology. Photon-counting detector technology has the capability to appreciably reduce CT imaging noise and improve spatial uniformity of noise values. In photon-counting CT, a model-based iterative reconstruction algorithm (QIR) allows decreasing effectively image noise. This is done without modifying noise histogram distribution shape, while limiting the low-frequency shift of noise power spectrum peak frequency.

Keywords: Algorithms; Image processing (computer-assisted); Radiation exposure; Tomography (x-ray computed); Tomography scanners (x-ray computed).

Fig. 1

Photon-counting detector computed tomography scanner: noise (HU) maps of CT image of the water phantom reconstructed using both FBP and QIR algorithm with increasing iterative power (i.e., Q1, Q2, Q3, and Q4), for the smoother (a) and sharper (b) reconstruction kernel. For these reconstruction kernels, different colormap ranges are used to better show differences when varying iterative power. FBP, Filtered back projection; QIR, Quantum iterative reconstruction

Fig. 2

Photon-counting detector computed tomography scanner: noise (HU) maps of CT image of the CTP404 module reconstructed using both FBP and QIR algorithm with increasing iterative power (i.e., Q1, Q2, Q3, and Q4), for the smoother (a) and sharper (b) reconstruction kernel. For these reconstruction kernels, different colormap ranges are used to better show differences when varying iterative power. FBP, Filtered back projection; QIR, Quantum iterative reconstruction

Fig. 3

Photon-counting detector computed tomography scanner: for the water phantom, maps of the percentage difference (%) between noise of FBP-reconstructed image and noise of QIR-reconstructed images with different iterative powers (i.e., Q1, Q2, Q3, and Q4), using the smoother (a) and sharper (b) reconstruction kernel. FBP, Filtered back projection; QIR, Quantum iterative reconstruction

Fig. 4

Photon-counting detector computed tomography scanner: for the CTP404 module, maps of the percentage difference (%) between noise of FBP-reconstructed image and noise of QIR-reconstructed images with different iterative powers (i.e., Q1, Q2, Q3, and Q4), using the smoother (a) and sharper (b) reconstruction kernel. FBP, Filtered back projection; QIR, Quantum iterative reconstruction

Fig. 5

Photon-counting detector computed tomography scanner: CTP404 phantom module with different contrast objects (i.e., air, PMP, LDPE, polystyrene, acrylic, Delrin, Teflon): noise (a, b) and percentage noise reduction (c, d) values (mean ± standard deviation within a 1 cm diameter region of interest placed in each contrast object), for FBP and QIR algorithm with increasing power (i.e., Q1, Q2, Q3, and Q4), as well as for the smoother (a, c) and sharper (b, d) kernel. FBP, Filtered back projection; LDPE, Low-density polyethylene; PMP, Polymethylpentene; QIR, Quantum iterative reconstruction

Fig. 6

For homogeneous water phantom (a) and CTP404 module (b), NUI values of noise maps of computed tomography images acquired on PCD-scanner and EID-scanner, reconstructed using different kernels and both FBP and iterative algorithms with increasing power (i.e., Q1/Q2/Q3/Q4 and S1/S2/S3/S4/S5 for PCD-scanner and EID-scanner, respectively). EID, Energy-integrating detector; FBP, Filtered back projection; NUI, Non-uniformity index; PCD, Photon-counting detector