Incorporating Tissue Composition Information in Total-Body PET Metabolic Quantification of Bone Marrow through Dual-Energy CT

TL;DR

This study introduces a bone fraction correction method using dual-energy CT to improve the accuracy of PET-based bone marrow metabolic quantification by accounting for tissue composition effects.

Contribution

The paper presents a novel BFC method that incorporates tissue composition from DECT into PET measurements, reducing underestimation of bone marrow activity.

Findings

BFC significantly increased SUV, K1, and Ki values in BM regions.

Voxel-wise material decomposition with DECT is feasible for metabolic BM imaging.

Current PET quantification methods likely underestimate BM activity without tissue composition correction.

Abstract

Bone marrow (BM) metabolic quantification with 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is of broad clinical significance for accurate assessment of BM at staging and follow-up, especially when immunotherapy is involved. However, current methods of quantifying BM may be inaccurate because the volume defined to measure bone marrow may also consist of a fraction of trabecular bone in which 18F-FDG activity is negligible, resulting in a potential underestimation of true BM uptake. In this study, we demonstrate this bone-led tissue composition effect and propose a bone fraction correction (BFC) method using X-ray dual-energy computed tomography (DECT) material decomposition. This study included ten scans from five cancer patients who underwent baseline and follow-up dynamic 18F-FDG PET and DECT scans using the uEXPLORER total-body PET/CT system. The voxel-wise bone volume fraction was estimated from DECT and then incorporated into the PET measurement formulas for BFC. The standardized uptake value (SUV), 18F-FDG delivery rate K1, and net influx rate Ki values in BM regions were estimated with and without BFC and compared using the statistical analysis. The results first demonstrated the feasibility of performing voxel-wise material decomposition using DECT for metabolic BM imaging. With BFC, the SUV, K1, and Ki values significantly increased by an average of 13.28% in BM regions compared to those without BFC (all P<0.0001), indicating the impact of BFC for BM quantification. Parametric imaging with BFC further confirmed regional analysis. Our study using DECT suggests current SUV and kinetic quantification of BM are likely underestimated in PET due to the presence of a significant bone volume fraction. Incorporating tissue composition information through BFC may improve BM metabolic quantification.