Accurate iodine quantification and residual error reduction with principal component analysis multimaterial decomposition using spectral CT

Background

Multimaterial decomposition (MMD) in dual-energy CT enables iodine quantification, critical for diagnostic applications. However, residual errors from noniodine materials in iodine maps limit accuracy, especially in complex thoracic regions and low-dose settings.

Purpose

To evaluate iodine quantification accuracy and residual error using a principal component analysis multimaterial decomposition (PCA-MMD) algorithm on dual-energy CT data across different phantom sizes and radiation dose levels.

Methods

A thorax phantom containing iodine (2–20 mg/mL) and calcium (50–400 mg/mL) inserts was scanned on a clinical photon-counting CT system. Three phantom sizes (small: 20.9 cm, medium: 27.3 cm, large: 33.2-cm water-equivalent diameter) were imaged at dose levels ranging from 3 to 55 mGy. The PCA-MMD algorithm applies a principal component analysis (PCA) transformation followed by direct geometric estimation with barycentric coordinates, thereby avoiding matrix inversion instability. Iodine quantification was evaluated using linear regression, root mean square error (RMSE), and coefficient of variation (CV). Residual error in noniodine regions was expressed as a percentage of the minimum detectable iodine concentration. The algorithm's performance was also evaluated through a clinical proof-of-concept study involving five patients, comparing virtual noncontrast (VNC) images to true noncontrast (TNC) references.

Results

PCA-MMD achieved near-unity regression slopes (0.98–0.99, $R^2\ge 0.996$) across all phantom sizes, reducing RMSE by up to 65% compared to the standard barycentric coordinate-based MMD (0.10–0.39 vs. 0.20–0.72 mg/mL). Residual error was markedly lower with PCA-MMD (0.7–1.6%) than with the standard barycentric coordinate-based MMD (16.1%–54.9%) under identical conditions. At 3 mGy, PCA-MMD achieved an RMSE of 0.60 mg/mL versus 0.95 mg/mL for the standard barycentric coordinate-based MMD. In the clinical cohort, PCA-MMD significantly improved VNC accuracy, achieving a mean RMSE of 15.5 HU compared to 20.9 HU for the vendor-specific algorithm ($p=0.012$). Reproducibility was excellent, with 85% of the measurements showing a CV .

Conclusions

PCA-MMD significantly improved iodine quantification accuracy and reduced residual error in both phantom and clinical settings. Its robustness across different dose levels supports its potential for clinical translation in quantitative dual-energy CT applications.