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

TL;DR

This work shows that voxel-level bone marrow quantification by FDG-PET is biased by surrounding trabecular bone, leading to underestimation of uptake. It introduces bone fraction correction (BFC) using dual-energy CT material decomposition and integrates v_{bone} into static and dynamic PET models, including a 2-tissue irreversible PK framework with time-delay correction. On total-body FDG-PET/CT data from five cancer patients, BFC increased SUV by roughly 10–20% and elevated $K_1$ and $K_i$ similarly, indicating significant underestimation with standard quantification. The approach, demonstrated via ROI analyses and parametric imaging, suggests improved bone marrow quantification with potential impact on cancer staging and immunotherapy assessment, and points to future clinical studies using PET-enabled DECT or expanded bone-marrow coverage.

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.

Figures (9)

• Figure 1: A graphical illustration of bone anatomy structure with spongy bone. A unit volume of “bone marrow” defined by PET may include actual bone marrow, trabeculae bone, and blood vessels.
• Figure 2: The 2-tissue irreversible compartmental model for describing $^{18}$F-FDG kinetics of bone marrow.
• Figure 3: Illustration of bone marrow ROI placement. (a) Automatic segmentation of spine and pelvis regions enabled by TotalSegmentator. (b) Manual ROI delineation based on (a). CT HU window: [-300, 1200]
• Figure 4: Visualization of X-ray DECT images (left, shown in HU window [-300, 1200]) and corresponding soft-tissue and bone fractional images overlaid on the 140 kVp CT image (right).
• Figure 5: Results of bone fractions for different regions of interest: cervical vertebra (CV), thoracic vertebra (TV), lumbar vertebra (LV), and pelvis. (a) Bar plots of bone fraction in different regions across all the ten scans. Only P values of paired t-test between CV and other regions were displayed. The other P values were all larger than 0.2. (b) Boxplot comparison of bone fraction between males and females using the unpaired t-test.