STEPC: A Multi-energy Nonuniform Response Calibration Framework for Photon-Counting Micro-CT in Multi-material Imaging

Abstract

Photon-counting computed tomography has demonstrated significant advancements in recent years; however, micro photon-counting CT (Micro-PCCT) systems are still limited by pixel-wise detector response nonuniformity, which degrades measurement uniformity across detector pixels and commonly produces ring artifacts in reconstructed images. Existing calibration methods exhibit limited generalizability in complex multi-material scenarios, such as contrast-enhanced imaging. This study introduces a Signal-to-Nonuniformity Error Polynomial Calibration (STEPC) framework based on measurement nonuniformity error modeling to address this issue. STEPC first fits multi-energy projections using a 2D polynomial surface to generate ideal references, then applies a nonlinear multi-energy polynomial model to predict and correct pixel-wise nonuniformity errors. The model is calibrated using homogeneous slab phantoms of different materials, including PMMA, aluminum, and iodinated contrast agents, enabling correction for both non-contrast and contrast-enhanced imaging. Experiments were performed on a custom Micro-PCCT system with phantoms and mouse. Correction performance of STEPC was evaluated using the mean local standard deviation (MLSD) in the projection domain and the ring artifact deviation (RAD) on the reconstructed images. Compared with existing methods, STEPC achieved an average MLSD reduction of at least 21.58% and reduced RAD by at least 14.18%, consistently yielding the best performance in both non-contrast and contrast-enhanced scenarios. Furthermore, STEPC can be readily extended to compensate for beam hardening effects within the same calibration framework. Quantitative material decomposition results indicate that the proposed method preserves measurement accuracy across different basis materials...

Figures (15)

• Figure 1: Distribution of spectral projection values for the non-contrast (a) and iodine-contrast (b) mouse scans.
• Figure 2: Workflow of the proposed STEPC method. Step 1: Estimation of ideal projections for calibration slab phantoms; Step 2: Calculation of pixel-wise nonuniformity errors; Step 3: Generation of the calibration table by fitting the empirical multi-energy polynomial model; Step 4: Projection correction for various imaging scenarios (red arrows indicate noticeable pixel response nonuniformity before correction).
• Figure 3: Schematic of the system geometry and multi-material slab phantoms. (a) Geometry configuration of the calibration setup; (b) three types of slab phantoms: PMMA, aluminum, and iodixanol; (c) schematic of iodixanol phantom container; (d) combinations of PMMA and aluminum slabs with varying thicknesses; (e) combinations of iodixanol solution slabs with PMMA and aluminum slabs.
• Figure 4: multi-material cylindrical phantoms. (a) Actual pictures of cylindrical phantoms; (b) CaCl$_2$ inserts with concentrations of 100, 200, 400, and 600 mg/mL insert in a PMMA holder; (c) Iodixanol and CaCl$_2$ inserts (iodixanol: 20, 50, 100 mg/mL; CaCl$_2$: 200, 400 mg/mL); (d) PMMA only; (e) 200 mg/mL CaCl$_2$ (2mm thickness PMMA cylindrical holder); (f) 50 mg/mL iodixanol.
• Figure 5: Illustration of the Ring Artifact Deviation (RAD) calculation. Left: Reconstructed image of a PMMA phantom shown in polar coordinates $(r, \theta)$. Right: Angularly averaged intensity profile. A second-order polynomial fit is used to generate the ideal baseline, which is subtracted from the real averaged profile to remove cupping artifacts.